Anonymous member


Career development 18h
Please i recently interviewed for a big firm , got to final stage interview and later received a rejection . i had previously looked up my interviewers ( 4 people ) on Linkedin and found them. is it a good idea to send them a connection request? i am really disappointed and pained as i had envisioned myself in the role already.
1   

  • John Heinrich

    47m

    Yes I'd send them a 'follow'. You need the feedback on why you didn't get hired, but you need a relationship with them first.

  • Nicole Anderson

    4h

    Im so sorry to hear that GOD will open a BIGGER door for you!

    4
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Stuart Harper


Career development 4d
I had a job interview last Wednesday. The HR person said they’d call me back in a few days. I know a week is normal to wait, but how long does it usually take to hear back? If they didn’t call soon, does that mean it’s a no? Thanks
1   

  • Pamela Koonce

    2h

    I experienced something similar, I REALLY wanted this job and I went through testing and 4 interviews... I have yet to hear back from them. I don't... know how to handle this as it's never happened to me before. I'm very sad about it! more

  • Gladys E. Bustos

    14h

    What do you do?

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Career development

 18h

Biggest Résumé Mistakes and How You Can Avoid Them


Going into my first year of college, I had no clue what a résumé even was. I had experience, but none of my previous jobs required me to submit a résumé when I applied. So, when my on-campus job asked for one, I scrambled to throw it together. I asked everyone I could for their help. With a horrible résumé (and a ton of grace given by my boss), I was hired.

Now, as a student assistant in the... Journalism Dean's Office, I review résumés daily. This is a list of the biggest mistakes I see in the office and how you can fix them to improve your résumé and chances of getting hired.

Contact information

Contact information is located beneath your name at the top of your résumé. This section includes your phone number, email address, LinkedIn, city and state and portfolio (if you have one).

More than one email address

The first mistake I see in the contact information section is including more than one email address. A lot of college students think it's best to list both their student email and personal email address to give the employer more options to choose from. While this is a good idea in theory, it can be confusing for employers to figure out the best way to contact you. Instead, list the email address that you check most frequently, whether that's personal or school. If you're a graduating student, you should list your personal email and make a habit of checking it regularly.

Not including LinkedIn

If you do not have a LinkedIn profile in college, you're doing it wrong. LinkedIn is an extremely important form of social media used for networking with people in your industry. Although it is understandable not to have a LinkedIn profile your first year of college, it is highly recommended that you create one before the beginning of your sophomore year.

The next step is putting the hyperlink to your profile in your contact section. Don't just link it to the word "LinkedIn;" copy and paste the full URL to ensure your profile can still be accessed easily if your resume were to be printed.

Including a picture

In the United States, federal law prohibits discrimination based on race, gender, ethnicity, age, etc. Including a picture on your résumé may trigger unconscious bias from your employer and prevent you from even making it to the interview stage. Some employers will even immediately reject résumés with photos to avoid potential discrimination accusations.

Education

This section is the most important information on your résumé as a college student. It includes your college, major, degree, GPA (if a 3.0 or above), expected graduation and minors or certificates, if applicable.

Getting your degree and major name wrong

This might be surprising to some, but in fact, many students get their degree and major wrong! All colleges have different degrees and major names, so it's important to check your school website for the official name of your degree.

High school information after your first year

As unfortunate as it is, employers don't care what you did in high school if you're a college student. It is much more important what you are doing in college, so high school should be completely omitted.

The exception to this rule is first-year college students. This is because until the end of the first semester of college, first-years do not have a GPA or much experience in their degree. That being said, it is generally recommended to remove your high school information from your education section after the first semester of freshman year, and definitely before the beginning of your sophomore year.

Experience

Your experience is the second most important information on your résumé. This section includes your past and present work experience with two to four detailed bullet points describing the work you did in each position, as well as the location and time frame you worked.

Missing detail

An important thing to remember when writing the bullet points for your experiences is to add detail! Employers don't just want to know what you did; they want to know how you did it. Instead of saying, "Wrote articles for Her Campus." You should say, "Wrote 6+ articles for Her Campus over topics of self-love, entertainment, culture, etc." This way of writing gives your employer a better understanding of your capabilities while quantifying your work and adding credibility.

Not including unpaid experiences

Unpaid experiences make up a large portion of a college student's experience. From internships to organizations, college students gain lots of unpaid experience. And many students think that because they did not earn a paycheck for these experiences, they cannot include them on their resume. That is not true. Employers care much more about the knowledge you have gained and experience you have in the position, rather than the amount of paid work you have.

Skills

Your skills section should always be the last section of your résumé. This section is a simple list of skills that you haven't expressed in your experience sections.

Soft skills

Your skills section should be solely hard skills. Things like teamwork, leadership and other soft skills are good to have, but they can easily be demonstrated in the bullet points of your experience section or in an interview.

Instead, include hard skills relevant to the job you are applying for. If you're a journalism major, your skills section should include things like AP style writing, video editing and photojournalism. You can also include programs that you are familiar with. Think Microsoft 360, Canva or Adobe. These kinds of skills will give your employer more information about the skills you possess.

Formatting

Although not a section, formatting your résumé the correct way is extremely important to the hiring process.

Using templates

As tempting as a super cute Canva or Word template is, do not give in! Most templates are formatted in a two-column style that doesn't scan well with applicant tracking systems (ATS). This means that your résumé could be thrown out before an actual human even takes a look at it. Instead, make your own one-column template that you can use over and over again.

Typos

This might sound like an obvious one, but it is so important to triple-check your résumé for spelling and grammar errors. Even one typo can get your résumé thrown in the trash. Employers tend to see typos as a liability later down the line. If you're not checking your résumé for misspellings, it signals to your employer that you'll make that mistake with important work as well.

More than one page

Résumés are recommended to be only one page in order to not overload your employer with unnecessary information. The average amount of time an employer spends reviewing a résumé is six to seven seconds. A résumé that is short and easy to read will allow your employer to focus less on trying to decipher your résumé and more on the skills you could bring to their team.

The most important thing to remember is that your résumé is a living document. This means that you can (and should) constantly be updating it. You should change your résumé for every application you submit.

Résumés are a hard skill to master, but once you understand the reasoning behind all the factors, it will all click and you'll have no trouble creating and editing your résumé. more
2   

Markus Robinson


Career development 1d
Hi! Do you trust your gut? I got the job after the interviews, but I feel like the employer isn’t telling me everything. It’s a weird feeling. I’m a bit scared to go to the office on Monday

  • peter elweu

    1d

    If you’ve learnt the art of trusting your gut and things have always worked out in your direction of the trust, it can apply in this situation as... well. Just don’t mistake the noise in your head for a gut feeling. more

  • Renata Fester

    1d

    First and foremost, be careful not to confuse new job jitters with suspicion s. Your new employer almost definitely did not tell you everything why... would he. You were not yet a member of staff so telling you anything about the business that is outside of a recruitment process would not have been shared. This is completely normal. 2ndly you are.most likely on a 3mnth probationary period, use it wisely. In as much as the company will be evaluating your fit for them, you should be evaluating their fit for you. If it doesn't feel right after a fair chance,you have every right to decline a permanent offer,or even not complete the probationary period without any complications.
    That said, dont simply dismiss your instinct because you habe no evidence of something being off. Often this is not as simple as something being inherently wrong, but a matter of an alignment or misalignment with what is truly important for you.at this stage in your career. Don't ignore that,its soul destroying.      more

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Mildred Priest


Career development 2d
Question for recruiters: Would you hire a VERY self-confident candidate? He made a good impression in the interview, and according to references he’s an excellent specialist. But I felt he was a bit arrogant and too independent. I wouldn’t want him to create problems in our well-coordinated team. What do you think?
2   

  • Pankaj Rathod

    10h

    Confidence comes from ability and knowledge in the field. If you are afraid of strong personalities, it is your weakness as a manager. Hire best... talent and manage them well. Anyways, weak personalities may be obedient, but do not add much value to the organisation. This is the key to progress. more

  • Renata Fester

    1d

    Arrogant? Very confident? Too independent? Its sounds like you've already cast judgement and decided you dont want these traits on your team. I wonder... why that is. This sounds like code for not easy to bully=arrogant; knows the job and will likely show me up=too confident; has sunstantial professional boundaries = too independent. One has to ask is this about a competent,confident team member coming on board OR is this about you feeling threatened by someone who has the potential to out perform you and the rest of the team and possibly raise the bar which could raise questions about current performance? Confident,competent managers always look for team members who are better than they are
    It makes them look good. The better question you should be asking is why does this bother me and why am I afraid of bringing this person on board.     more

    1
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Career development

 15h

AI-Powered Hiring System Development


I'm building a web-based platform that screens applicants automatically. The core workflow needs to: * parse incoming résumés/CVs, * embed both the résumé data and our job descriptions, * run a retrieval-augmented generation pipeline that compares the two, * return a clear numerical fit score so the recruiter can decide whether to proceed. All candidate data will be embedded with FAISS, while... LangChain will orchestrate the retrieval and callouts to the OpenAI GPT endpoints. I already have the OpenAI key and hosting in place; what's missing is the complete back-end logic, the scoring algorithm, and a simple front-end view that lists the ranked applicants and their matching rationale. Deliverables 1. End-to-end web application (React or similar front end, Python back end) running locally in Docker. 2. Resume parser that extracts structured data (education, skills, experience) ready for vectorisation. 3. RAG module using LangChain + FAISS that produces the fit score and a short explanation paragraph. 4. REST endpoints documented with Swagger/OpenAPI. 5. Brief deployment guide and code walkthrough. Acceptance criteria: given a sample résumé and job description, the system must return a repeatable score between 0-100 plus an explanation, all under 5 seconds. If you've shipped similar NLP or hiring-tech projects and are comfortable with LangChain, FAISS, and OpenAI, I'd love to see a link or demo in your proposal. more

Career development

 16h

Mastering Difficult Interview Questions for Career Success


In today's competitive job market, acing an interview is crucial for career advancement, but many candidates find themselves stumped by difficult interview questions. Understanding how to navigate tough job interview questions can significantly enhance your chances of landing your desired role. These questions are designed to assess not just your skills and experience but also your problem-solving... abilities and how you handle pressure.

Understanding Difficult Interview Questions

Difficult interview questions come in many forms, including behavioral questions, hypothetical scenarios, and questions that test your technical expertise. Employers use these challenging interview questions to evaluate various competencies and to see how well you articulate your thoughts under stress.

Common Types of Tough Interview Questions and Answers

Preparation is key to confidently dealing with the toughest interview questions. Here are some common types you may encounter:

* Behavioral Questions: These questions often begin with phrases such as "Tell me about a time when..." and are designed to explore your past behavior in work-related situations. For example, "Tell me about a time when you had to meet a tight deadline." Candidates should use the STAR method (Situation, Task, Action, Result) to structure their responses.

* Hypothetical Questions: Employers might ask you to imagine handling a specific scenario, such as "How would you handle a conflict with a coworker?" This helps interviewers assess your problem-solving skills.

* Self-Assessment Questions: These tough job interview questions focus on your reflections about yourself, including "What is your biggest weakness?" The key is to choose a real weakness and show how you are managing or improving upon it.

Strategies for Answering Challenging Interview Questions

When preparing for an interview, consider the following strategies to tackle difficult interview questions and answers effectively:

* Research: Understand the company and the role thoroughly. This knowledge allows you to tailor your responses to align with the company's culture and expectations.

* Practice: Rehearse your answers to common tough interview questions with a friend or mentor who can provide feedback.

* Stay Calm: Take a moment to gather your thoughts before answering. It's better to deliver a well-thought-out response than to rush and make mistakes.

For more guidance on related employment topics, you can explore our article on crafting an employment gap explanation letter. This can help you present yourself more favorably in difficult circumstances.

Adapting Your Approach Based on the Question Type

Knowing how to adapt your approach for different question types can also be beneficial. Here's how you can tackle specific types of tough job interview questions:

* Technical Questions: These require you to demonstrate your technical skills. If you don't know the answer, show your problem-solving process and willingness to learn.

* Situational Questions: Focus on demonstrating your critical thinking and how you might apply your skills in real-world scenarios.

* Abstract Questions: Such as "If you were an animal, what would you be?" These test your creativity, so think on your feet and link your choice to role-relevant qualities.

The Importance of Follow-Up

After handling difficult interview questions, don't forget the importance of follow-up. Sending a thoughtful thank-you note reiterating your interest in the role and mentioning specific points from the interview can leave a positive impression. It's a great way to demonstrate professionalism and remind the interviewers of your qualifications.

By mastering these strategies, you can turn the toughest interview questions into opportunities to showcase your strengths and achievements. For further reading on educational topics that can support your career aspirations, consider visiting reputable sources like this comprehensive overview on education.

Conclusion

Mastering difficult interview questions is a crucial skill for career success. By understanding the types of questions you're likely to encounter and preparing your responses in advance, you can handle even the most challenging interview questions with confidence. Remember, interviews are not just about evaluating if you're the right fit for the company; they're also about you assessing if the company is the right place for you to grow your career.

* Researching the company can give you a competitive edge.

* Preparing and practicing are essential to handling difficult questions.

* Immediate calm and composure during interviews improve your performance.

* Following up after an interview leaves a lasting positive impression.

FAQ

How should I prepare for difficult interview questions?

Start by researching the company and understanding the job description. Practice answering common question types with a friend or mentor to get constructive feedback.

What is the STAR method?

The STAR method is a structured way to respond to behavioral questions by outlining the Situation, Task, Action, and Result, which helps interviewers understand your past experiences.

Why do employers ask hypothetical questions?

Hypothetical questions are used to gauge how you might handle future situations and your problem-solving abilities. They help employers determine how you think on your feet.

What if I don't know the answer to a technical question?

Explain your thought process and how you plan to find a solution. Employers value candidates who demonstrate problem-solving skills and the ability to learn.

Should I send a thank-you note after an interview?

Yes, sending a thank-you note reaffirms your interest in the role and allows you to highlight important discussion points from the interview, reinforcing your suitability for the position. more

Career development

 16h

Crafting the Perfect Electrician Resume for Career Success


In today's competitive job market, an effective electrician resume can be the key to securing that coveted job interview. Whether you're a seasoned professional or just starting in the field, crafting a resume that highlights your skills and experience is crucial. By understanding the essential elements that make up successful electrician resumes, you can create a document that stands out to... potential employers.

Key Elements of Electrician Resumes

An electrician resume should effectively showcase your technical skills, relevant experience, and professional accomplishments. This document should be tailored to each job application, emphasizing the qualifications that make you an ideal candidate for the specific role.

Contact Information

Your resume should begin with your full name and accurate contact information, including phone number and email address. Make sure this section is up-to-date and professional, as it serves as the primary way for employers to reach you.

Professional Summary

Include a concise professional summary that highlights your key strengths and career achievements. This section should provide an overview of your expertise and convey your enthusiasm for the role.

Skills and Qualifications

List out your core competencies, such as technical skills and certifications, that align with the typical job requirements of an electrician. Highlight skills that include knowledge of electrical codes, proficiency in using diagnostic tools, and experience with installation and maintenance.

Work Experience

Detail your work history in reverse chronological order, beginning with your most recent position. Include company names, job titles, and employment dates. Emphasize accomplishments and responsibilities that demonstrate your value, such as successful project completions and safety compliance.

Education and Certifications

Include your educational background, focusing on relevant degrees or vocational training. Don't forget to mention any certifications that enhance your qualifications, such as journeyman or master electrician licenses. If you're looking for more advice on handling resume gaps, check out our guide on explaining employment gaps effectively.

A Sample Electrician Resume

Creating a sample electrician resume can serve as a helpful guide. Here's a brief example for reference:

* Name: John Doe

* Professional Summary: Experienced electrician with over 10 years in residential, commercial, and industrial settings. Proven ability to manage complex electrical projects from planning to execution.

* Skills: Electrical safety compliance, blueprint interpretation, troubleshooting and diagnostics.

* Experience:

* Education: Associate Degree in Electrical Engineering, State Technical College

* Certifications: Licensed Journeyman Electrician

This resume format for an electrician is structured to clearly demonstrate expertise and growth in the field.

Important Tips

When preparing your electrician resume, keep these points in mind:

1. Tailor Each Resume: Modify your resume to reflect the unique requirements of each job application. Focus on relevant experience and skills.

2. Use Clear Formatting: Organize your resume with clear headers and bullet points for easy reading. Utilize a professional layout that emphasizes your strengths.

3. Quantify Achievements: Wherever possible, use numbers to highlight your accomplishments, such as the number of projects completed or safety improvements implemented.

Electrician Resume Example Considerations

When using a sample electrician resume as a model, remember to personalize it with your own experiences and achievements. Avoid copying content directly, and ensure that your resume reflects your unique qualifications and career trajectory.

Conclusion

In crafting a compelling electrician resume, your aim should be to effectively communicate your skills and experiences to potential employers. By tailoring your resume to each position, using a clear format, and showcasing your achievements, you greatly increase your chances of career success. For further guidance, consider exploring the Bureau of Labor Statistics for detailed insights into the electrician profession.

By focusing on these key elements, your electrician resume can help pave the way to new opportunities in the dynamic field of electrical work.

* Tailor your resume for each job application.

* Use a professional layout with clear headings.

* Quantify achievements to highlight value.

* Include relevant education and certifications.

* Refer to credible sources for industry insights.

FAQ

What should I include in my electrician resume?

Include contact information, a professional summary, skills, work experience, and education and certifications. Tailor the content to match the specific job requirements.

How can I make my electrician resume stand out?

Focus on using clear formatting, tailoring each application, and highlighting your unique accomplishments and skills. Quantifying achievements can also add impact.

What is a good resume format for an electrician?

A chronological format is typically effective, presenting your experience in reverse order and allowing employers to easily track your career progress. Make sure to use headings and bullet points for clarity.

Why is tailoring a resume important?

Tailoring your resume ensures that you emphasize the most relevant skills and experiences for each job, making you a more attractive candidate to potential employers.

Where can I find more guidance on improving my resume?

You can access resources like our employment gap explanation guide for additional assistance. External resources such as the Bureau of Labor Statistics may also offer useful insights. more

Career development

 19h

Majority of College Students Represent a "Blank Slate" to AI Systems, New Lilypath Data Shows


NEW YORK, May 07, 2026 (GLOBE NEWSWIRE) -- College students are graduating into a hiring market they can't see, and that can't see them. New data from Authority Intelligence™ platform Lilypath shows that the majority of college students score below 39 out of 100 on its proprietary AI Readiness Score, landing in what the company calls the "Blank Slate" bracket. Translation: the automated systems... that now sit between graduates and recruiters can barely register them at all.

The finding arrives at a precarious moment for both students and the institutions that prepare them. Recent U.S. college graduates faced 5.8% unemployment in 2025, the worst in over a decade outside the pandemic, and 42.5% were underemployed, working in jobs that don't require their degree, according to the Federal Reserve Bank of New York. At the same time, nearly all Fortune 500 employers (99%) now use AI-powered applicant tracking systems to filter candidates before a human ever reviews a résumé, according to Jobscan.

"Students are doing everything they've been told to do, building résumés, working with career centers, gaining experience, but the hiring process has changed underneath them," said Erin Lanuti, Co-Founder and CEO of Lilypath. "If AI can't understand who you are, you won't be considered. We're teaching the next generation how to be seen by the systems now deciding their future."

Why This Is a University and Student Problem

Career services as traditionally structured no longer translate to AI placement readiness. Colleges that fail to address AI visibility face declining placement outcomes, a metric that directly affects institutional reputation, U.S. News rankings, and the recruitment of the next class. In an era when famlies increasingly weigh ROI before tuition, weak job-placement numbers compound quickly.

"As a faculty member, I see firsthand how unclear the job search process has become for students," said John Murphy, Assistant Professor in-Residence of Digital Media Business Strategies at the University of Connecticut. "Lilypath brings much-needed clarity and gives students the tools to take control of how they show up in an increasingly AI-driven hiring landscape."

The "Blank Slate" Finding

Lilypath's AI Readiness Score evaluates LinkedIn profiles, the front door most recruiters and AI sourcing tools now use, across five categories that determine how AI systems rank and surface candidates. When applied to college students, the pattern was consistent and stark: most aren't underperforming on LinkedIn, they're effectively absent from it.

"It has been shocking to us just how low their scores are, and the immense opportunity cost that brings," Lanuti said. "These are accomplished, highly capable students. They're just invisible to the algorithm."

What Lilypath Delivers

Lilypath is a patent-pending Authority Intelligence™ platform that governs how AI systems interpret professional credibility and expertise. For students, the platform produces a personalized Blueprint that includes:

* An AI Readiness Score (0-100) benchmarked against peers and industry

* Section-by-section diagnostics for Headline, About, Experience, and Skills

* Copy-ready rewrites students can implement immediately

* Strategic positioning guidance aligned to the student's career goals

The experience is built for speed: a 5-minute intake, a personalized Blueprint, and clear actionable guidance to update the LinkedIn profile.

University Pilots Already Underway

Lilypath is partnering with colleges and universities to embed AI visibility into career readiness curricula, with initial pilot programs focused on graduating seniors. Universities interested in pilot participation can contact Lilypath directly.

"AI now sits between students and opportunity," Lanuti added. "The institutions that recognize this first will help their graduates land real jobs, and protect the placement outcomes their reputations depend on."

Students can learn more and access Lilypath's student offering at lilypath.com/student. Universities interested in pilot programs can contact Lilypath at [email protected].

About Lilypath

Lilypath is a patent-pending Authority Intelligence™ platform that helps individuals understand how AI systems interpret and represent their professional identity. Through a 0-100 AI Readiness Score, section-by-section diagnostics, and copy-ready recommendations, Lilypath enables students, professionals, and executives to improve their visibility, credibility, and positioning in an AI-mediated world. Learn more at lilypath.com. more

Career development

 20h

Majority of College Students Represent a "Blank Slate" to AI Systems, New Lilypath Data Shows


NEW YORK, May 07, 2026 (GLOBE NEWSWIRE) -- College students are graduating into a hiring market they can't see, and that can't see them. New data from Authority Intelligence™ platform Lilypath shows that the majority of college students score below 39 out of 100 on its proprietary AI Readiness Score, landing in what the company calls the "Blank Slate" bracket. Translation: the automated systems... that now sit between graduates and recruiters can barely register them at all.

The finding arrives at a precarious moment for both students and the institutions that prepare them. Recent U.S. college graduates faced 5.8% unemployment in 2025, the worst in over a decade outside the pandemic, and 42.5% were underemployed, working in jobs that don't require their degree, according to the Federal Reserve Bank of New York. At the same time, nearly all Fortune 500 employers (99%) now use AI-powered applicant tracking systems to filter candidates before a human ever reviews a résumé, according to Jobscan.

"Students are doing everything they've been told to do, building résumés, working with career centers, gaining experience, but the hiring process has changed underneath them," said Erin Lanuti, Co-Founder and CEO of Lilypath. "If AI can't understand who you are, you won't be considered. We're teaching the next generation how to be seen by the systems now deciding their future."

Why This Is a University and Student Problem

Career services as traditionally structured no longer translate to AI placement readiness. Colleges that fail to address AI visibility face declining placement outcomes, a metric that directly affects institutional reputation, U.S. News rankings, and the recruitment of the next class. In an era when families increasingly weigh ROI before tuition, weak job-placement numbers compound quickly.

"As a faculty member, I see firsthand how unclear the job search process has become for students," said John Murphy, Assistant Professor in-Residence of Digital Media Business Strategies at the University of Connecticut. "Lilypath brings much-needed clarity and gives students the tools to take control of how they show up in an increasingly AI-driven hiring landscape."

The "Blank Slate" Finding

Lilypath's AI Readiness Score evaluates LinkedIn profiles, the front door most recruiters and AI sourcing tools now use, across five categories that determine how AI systems rank and surface candidates. When applied to college students, the pattern was consistent and stark: most aren't underperforming on LinkedIn, they're effectively absent from it.

"It has been shocking to us just how low their scores are, and the immense opportunity cost that brings," Lanuti said. "These are accomplished, highly capable students. They're just invisible to the algorithm."

What Lilypath Delivers

Lilypath is a patent-pending Authority Intelligence™ platform that governs how AI systems interpret professional credibility and expertise. For students, the platform produces a personalized Blueprint that includes:

* An AI Readiness Score (0-100) benchmarked against peers and industry

* Section-by-section diagnostics for Headline, About, Experience, and Skills

* Copy-ready rewrites students can implement immediately

* Strategic positioning guidance aligned to the student's career goals

The experience is built for speed: a 5-minute intake, a personalized Blueprint, and clear actionable guidance to update the LinkedIn profile.

University Pilots Already Underway

Lilypath is partnering with colleges and universities to embed AI visibility into career readiness curricula, with initial pilot programs focused on graduating seniors. Universities interested in pilot participation can contact Lilypath directly.

"AI now sits between students and opportunity," Lanuti added. "The institutions that recognize this first will help their graduates land real jobs, and protect the placement outcomes their reputations depend on."

Students can learn more and access Lilypath's student offering at lilypath.com/student. Universities interested in pilot programs can contact Lilypath at hello@lilypath.com.

About Lilypath

Lilypath is a patent-pending Authority Intelligence™ platform that helps individuals understand how AI systems interpret and represent their professional identity. Through a 0-100 AI Readiness Score, section-by-section diagnostics, and copy-ready recommendations, Lilypath enables students, professionals, and executives to improve their visibility, credibility, and positioning in an AI-mediated world. Learn more at lilypath.com. more

Career development

 20h

Majority of College Students Represent a "Blank Slate" to AI Systems, New Lilypath Data Shows


Lilypath's Authority Intelligence™ platform reveals most students are nearly invisible on LinkedIn, the front door to AI-driven hiring, threatening placement outcomes that determine university rankings and enrollment

NEW YORK, May 07, 2026 (GLOBE NEWSWIRE) -- College students are graduating into a hiring market they can't see, and that can't see them. New data from Authority Intelligence™... platform Lilypath shows that the majority of college students score below 39 out of 100 on its proprietary AI Readiness Score, landing in what the company calls the "Blank Slate" bracket. Translation: the automated systems that now sit between graduates and recruiters can barely register them at all.

The finding arrives at a precarious moment for both students and the institutions that prepare them. Recent U.S. college graduates faced 5.8% unemployment in 2025, the worst in over a decade outside the pandemic, and 42.5% were underemployed, working in jobs that don't require their degree, according to the Federal Reserve Bank of New York. At the same time, nearly all Fortune 500 employers (99%) now use AI-powered applicant tracking systems to filter candidates before a human ever reviews a résumé, according to Jobscan.

"Students are doing everything they've been told to do, building résumés, working with career centers, gaining experience, but the hiring process has changed underneath them," said Erin Lanuti, Co-Founder and CEO of Lilypath. "If AI can't understand who you are, you won't be considered. We're teaching the next generation how to be seen by the systems now deciding their future."

Why This Is a University and Student Problem

Career services as traditionally structured no longer translate to AI placement readiness. Colleges that fail to address AI visibility face declining placement outcomes, a metric that directly affects institutional reputation, U.S. News rankings, and the recruitment of the next class. In an era when families increasingly weigh ROI before tuition, weak job-placement numbers compound quickly.

"As a faculty member, I see firsthand how unclear the job search process has become for students," said John Murphy, Assistant Professor in-Residence of Digital Media Business Strategies at the University of Connecticut. "Lilypath brings much-needed clarity and gives students the tools to take control of how they show up in an increasingly AI-driven hiring landscape."

The "Blank Slate" Finding

Lilypath's AI Readiness Score evaluates LinkedIn profiles, the front door most recruiters and AI sourcing tools now use, across five categories that determine how AI systems rank and surface candidates. When applied to college students, the pattern was consistent and stark: most aren't underperforming on LinkedIn, they're effectively absent from it.

"It has been shocking to us just how low their scores are, and the immense opportunity cost that brings," Lanuti said. "These are accomplished, highly capable students. They're just invisible to the algorithm."

What Lilypath Delivers

Lilypath is a patent-pending Authority Intelligence™ platform that governs how AI systems interpret professional credibility and expertise. For students, the platform produces a personalized Blueprint that includes:

The experience is built for speed: a 5-minute intake, a personalized Blueprint, and clear actionable guidance to update the LinkedIn profile.

University Pilots Already Underway

Lilypath is partnering with colleges and universities to embed AI visibility into career readiness curricula, with initial pilot programs focused on graduating seniors. Universities interested in pilot participation can contact Lilypath directly.

"AI now sits between students and opportunity," Lanuti added. "The institutions that recognize this first will help their graduates land real jobs, and protect the placement outcomes their reputations depend on."

Students can learn more and access Lilypath's student offering at lilypath.com/student. Universities interested in pilot programs can contact Lilypath at hello@lilypath.com.

About Lilypath

Lilypath is a patent-pending Authority Intelligence™ platform that helps individuals understand how AI systems interpret and represent their professional identity. Through a 0-100 AI Readiness Score, section-by-section diagnostics, and copy-ready recommendations, Lilypath enables students, professionals, and executives to improve their visibility, credibility, and positioning in an AI-mediated world. Learn more at lilypath.com. more

Career development

 20h

Majority of College Students Represent a 'Blank Slate" to AI Systems, New Lilypath Data Shows


Lilypath's Authority Intelligence™ platform reveals most students are nearly invisible on LinkedIn, the front door to AI-driven hiring, threatening placement outcomes that determine university rankings and enrollment

NEW YORK, May 07, 2026 (GLOBE NEWSWIRE) -- College students are graduating into a hiring market they can't see, and that can't see them. New data from Authority Intelligence™... platform Lilypath shows that the majority of college students score below 39 out of 100 on its proprietary AI Readiness Score, landing in what the company calls the "Blank Slate" bracket. Translation: the automated systems that now sit between graduates and recruiters can barely register them at all.

The finding arrives at a precarious moment for both students and the institutions that prepare them. Recent U.S. college graduates faced 5.8% unemployment in 2025, the worst in over a decade outside the pandemic, and 42.5% were underemployed, working in jobs that don't require their degree, according to the Federal Reserve Bank of New York. At the same time, nearly all Fortune 500 employers (99%) now use AI-powered applicant tracking systems to filter candidates before a human ever reviews a résumé, according to Jobscan.

"Students are doing everything they've been told to do, building résumés, working with career centers, gaining experience, but the hiring process has changed underneath them," said Erin Lanuti, Co-Founder and CEO of Lilypath. "If AI can't understand who you are, you won't be considered. We're teaching the next generation how to be seen by the systems now deciding their future."

Why This Is a University and Student Problem

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Career services as traditionally structured no longer translate to AI placement readiness. Colleges that fail to address AI visibility face declining placement outcomes, a metric that directly affects institutional reputation, U.S. News rankings, and the recruitment of the next class. In an era when families increasingly weigh ROI before tuition, weak job-placement numbers compound quickly.

"As a faculty member, I see firsthand how unclear the job search process has become for students," said John Murphy, Assistant Professor in-Residence of Digital Media Business Strategies at the University of Connecticut. "Lilypath brings much-needed clarity and gives students the tools to take control of how they show up in an increasingly AI-driven hiring landscape."

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The "Blank Slate" Finding

Lilypath's AI Readiness Score evaluates LinkedIn profiles, the front door most recruiters and AI sourcing tools now use, across five categories that determine how AI systems rank and surface candidates. When applied to college students, the pattern was consistent and stark: most aren't underperforming on LinkedIn, they're effectively absent from it.

"It has been shocking to us just how low their scores are, and the immense opportunity cost that brings," Lanuti said. "These are accomplished, highly capable students. They're just invisible to the algorithm."

What Lilypath Delivers

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Lilypath is a patent-pending Authority Intelligence™ platform that governs how AI systems interpret professional credibility and expertise. For students, the platform produces a personalized Blueprint that includes:

* An AI Readiness Score (0-100) benchmarked against peers and industry

* Section-by-section diagnostics for Headline, About, Experience, and Skills

* Copy-ready rewrites students can implement immediately

* Strategic positioning guidance aligned to the student's career goals The experience is built for speed: a 5-minute intake, a personalized Blueprint, and clear actionable guidance to update the LinkedIn profile.

University Pilots Already Underway

Lilypath is partnering with colleges and universities to embed AI visibility into career readiness curricula, with initial pilot programs focused on graduating seniors. Universities interested in pilot participation can contact Lilypath directly.

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"AI now sits between students and opportunity," Lanuti added. "The institutions that recognize this first will help their graduates land real jobs, and protect the placement outcomes their reputations depend on."

Students can learn more and access Lilypath's student offering at lilypath.com/student. Universities interested in pilot programs can contact Lilypath at [email protected].

About Lilypath

Lilypath is a patent-pending Authority Intelligence™ platform that helps individuals understand how AI systems interpret and represent their professional identity. Through a 0-100 AI Readiness Score, section-by-section diagnostics, and copy-ready recommendations, Lilypath enables students, professionals, and executives to improve their visibility, credibility, and positioning in an AI-mediated world. Learn more at lilypath.com. more

Career development

 21h

The AI Governance Triad: Why ISO 42001, NIST AI RMF, and the EU AI Act Are No Longer Optional | DISC InfoSec blog


The AI Governance Triad: Why ISO 42001, NIST AI RMF, and the EU AI Act Are No Longer Optional

Three frameworks, one imperative -- and a closing window for organizations that want to lead rather than catch up.

AI is being deployed inside enterprises faster than any technology in the last twenty years. Procurement is signing SaaS contracts with embedded large language models. Engineering teams are... wiring autonomous agents into customer workflows. HR platforms are scoring résumés. Marketing is generating campaign content at scale. Most boards have not yet asked the question that defines the next twenty-four months: what is our AI risk posture, and who owns it? Until that question has a clear answer -- backed by evidence a regulator or enterprise customer would accept -- the organization is operating on borrowed time.

The EU AI Act is the first comprehensive AI law with genuine extraterritorial reach. Its penalty structure makes the stakes legible: up to €35 million or 7% of global turnover for using prohibited AI practices, up to €15 million or 3% for high-risk system violations, and up to €7.5 million or 1% for procedural and technical breaches. The Act classifies systems by risk -- unacceptable, high, limited, minimal -- and assigns distinct obligations to providers, deployers, importers, distributors, authorized representatives, and product manufacturers. If your AI touches EU users, you are in scope, regardless of where your headquarters sit. The August 2026 high-risk deadline is no longer a planning horizon. It is a delivery date.

ISO/IEC 42001 is the world's first certifiable AI management system standard, and it is doing for AI governance what ISO 27001 did for information security: turning a diffuse set of "best practices" into an auditable, repeatable management system built around policy, risk assessment, controls, internal audit, management review, and continuous improvement. ISO 42001 is the artifact that lets you prove -- to a regulator, a customer's procurement team, an investor in diligence -- that AI governance exists as an operating system inside the company, not as a slide deck on a shared drive. Certification is the credibility multiplier.

NIST AI RMF complements ISO 42001 from a different angle. It is voluntary, U.S.-originated, and engineering-grade. Its four functions -- Govern, Map, Measure, Manage -- translate the abstract idea of "trustworthy AI" into testable practice: bias measurement, robustness testing, lifecycle documentation, incident response, and continuous monitoring. NIST AI RMF is not audit-bearing on its own, but it provides the technical scaffolding that makes ISO 42001 controls actually implementable and EU AI Act conformity assessments actually defensible under scrutiny.

These three frameworks are not alternatives. They occupy different layers of the same stack. The EU AI Act is the legal floor -- what you must do to operate. ISO 42001 is the management system -- how you govern AI consistently across the organization. NIST AI RMF is the technical risk practice -- how engineers and product teams operationalize trustworthiness in real systems. Treating them as a menu of choices is a category error that will surface during your first regulator inquiry, your first enterprise security questionnaire, or your first AI incident. A credible program touches all three.

The shared vocabulary across the three is not accidental. Transparency, traceability, explainability, human oversight, data minimization, fairness, accountability -- these principles appear in all three frameworks because they are the conversion mechanism that turns "we use AI" from a liability disclosure into a competitive differentiator. Buyers in regulated industries -- financial services, healthcare, life sciences, M&A advisory, anything touching personal data -- are already asking "how do you govern your AI?" before they sign. A coherent, evidenced answer wins enterprise deals. A hand-wave loses them.

The sector reality is sharper than most leadership teams realize. Recruitment AI, employee monitoring, admissions and grading, exam proctoring, credit scoring, insurance pricing, medical diagnostics, patient monitoring, lane-keeping and collision avoidance, biometric identification -- every one of these is classified as high-risk or outright prohibited under the AI Act. Many organizations are operating these systems today without having mapped them, without a Fundamental Rights Impact Assessment, without a conformity assessment plan. The gap between "we have an AI acceptable use policy" and "we can produce a defensible risk file for this specific system within forty-eight hours of a regulatory request" is precisely where enforcement action will concentrate.

The cost calculus has inverted. Five years ago, AI governance was insurance -- overhead with no visible payoff and no procurement signal behind it. Today the inverse holds: a single misclassified high-risk system can produce a €15M fine, contractual clawbacks from enterprise customers, public incident disclosure, and board-level scrutiny that consumes leadership attention for quarters. The fully-loaded cost of an ISO 42001 implementation -- assessment, gap remediation, internal audit, certification -- is a small fraction of a single regulatory action and a smaller fraction still of a lost enterprise contract. More importantly, it builds the organizational muscle to ship AI faster, because every new deployment runs through a known set of controls rather than triggering bespoke legal review.

Early movers compound. The organizations that stand up an AI Management System in 2026 will, within twenty-four months, be selling into procurement processes that explicitly require one. The pattern is identical to the one ISO 27001 followed: certification moved from "differentiator" to "table stakes" inside three years, and the vendors who waited spent the next two years catching up while their competitors took market share. ISO 42001 is on the same trajectory -- accelerated, because the regulatory pressure behind it is heavier and the customer concern about AI is sharper than it ever was about cloud security.

My perspective. As a practitioner who has led an ISO 42001 implementation through Stage 2 certification -- and who consults for organizations building AI governance programs from scratch -- I will be direct. The question is no longer whether to comply. It is which framework you anchor on first, and how quickly you can produce evidence under it. My recommendation is consistent across every engagement: anchor on ISO 42001 as the management system spine, adopt NIST AI RMF as the technical risk and measurement practice, and treat EU AI Act conformity as the regulatory floor -- even if you have no EU exposure today, because every other major jurisdiction is converging on the same architectural shape. The organizations that get this right in the next twelve months will not merely avoid penalties. They will own the customer trust position in a market that is about to be redrawn around exactly this question.

Author bio block -- DISC InfoSec | ISO 42001, ISO 27001, EU AI Act compliance | www.DeuraInfoSec.com

The AI Governance Quick-Start: Defensible in 10 Days, Not 4 Quarters

DISC InfoSec is an active ISO 42001 implementer and PECB Authorized Training Partner specializing in AI governance for B2B SaaS and financial services organizations. more

Career development

 23h

A Human in the Loop: The Future of AI in Arbitration


In today's legal landscape, artificial intelligence (AI) is no longer a distant or speculative concept. Instead, it has morphed into an everyday, practical tool for dispute resolution, especially in arbitration. Although technology-assisted processes have supported arbitral practice for decades, the emergence of generative AI represents a material shift in its application. These systems can... generate text, summaries, and analytical outputs by identifying patterns across vast datasets, offering new possibilities for efficiency, cost control, and decision support throughout the arbitral process. As its capabilities continue to expand, the question becomes: how much of our arbitral practice can AI contribute to a more efficient process, and what part of decision-making still requires what commentators increasingly refer to as "a human in the loop"?[1]

The use of AI in arbitration already spans a wide range of applications. At the most limited level, AI tools are employed to assist parties or arbitrators with discrete tasks, such as summarizing submissions, organizing evidence, or reviewing procedural materials. At an intermediate level, AI has been introduced into more consequential procedural functions, including the selection or ranking of arbitrators based on data-driven criteria. At the most expansive end of the spectrum, proposals and pilot programs envision AI playing a direct role in decision-making itself, including the development of AI-assisted or AI-driven arbitral models within institutional frameworks.

As these applications become more sophisticated, they raise concerns that extend beyond efficiency alone. Questions of transparency, explainability, bias, accountability, and human oversight, move from the abstract to the concrete once AI tools influence procedural choices or substantive outcomes. Their deployment implicates essential procedural guarantees, including due process, equality of arms, disclosure obligations, and the parties' confidence in the legitimacy of the arbitral process and award. And when it comes to substantive choices, the implicit bias of inputs and lack of transparency in the process call into question: how many of our systems are irreplaceably human? The future of AI in arbitration will therefore be shaped less by whether AI is used at all and more by how firmly human judgment is kept "in the loop" at each stage of the arbitral lifecycle.

This article examines the use of AI in arbitration across this spectrum of small-, medium-, and large-scale applications. It compares the distinct characteristics, challenges, and implications associated with each approach and considers how AI may be integrated into arbitral practice in a manner that strengthens, rather than erodes, fairness, procedural integrity, transparency, and confidence in arbitral decision-making.

I. Support, Not Substitution: Limited Uses of AI in Arbitration

At the most limited end of the spectrum, AI is deployed as a support tool rather than a decision-maker. In international arbitration, this use most commonly arises in the review and organization of client documents, bundle preparation, and procedural case management. AI tools can classify documents, extract relevant information, suggest targeted search terms, and identify factual or thematic patterns across large datasets almost instantaneously. When integrated into existing workflows, these applications can materially enhance efficiency, reduce cost, and accelerate proceedings, allowing counsel and arbitrators to devote greater attention to legal analysis, procedural strategy, and the merits of the dispute.

Limited-use AI is also increasingly employed as a legal research and drafting aid. Large language models (LLMs) can rapidly identify relevant case law and statutory authorities, summarize complex legal doctrines, synthesize applicable legal standards, and generate primers on unfamiliar subject matter. These capabilities are now embedded in established legal research platforms, including Westlaw, where AI-driven functionality operates within closed, source-verified databases. Used properly, such tools can streamline early-stage research and sharpen issue identification, but they still do not replace legal reasoning or professional judgment.

Practitioners seeking to get started should confine initial use to clearly defined tasks and adopt disciplined prompting techniques. Prompts should narrowly specify the scope of the request, restrict the AI to identified materials or databases, and require the provision of complete, verifiable citations. Instructions to summarize "without adding facts," to quote directly from supplied documents, or to identify authorities "only if traceable to an existing source" can materially reduce the risk of fabricated output. These prompting strategies are, in effect, mechanisms for keeping human control inside the loop -- structuring how AI is used, constraining its sources, and signaling that the ultimate responsibility for accuracy remains with the practitioner.

The risks associated with even these restrained applications are no longer theoretical. A significant number of instances have been reported in state and federal courts in which attorneys were sanctioned for filing briefs containing hallucinated citations. In a recent opinion underscoring the ethical risks of uncritical reliance on generative tools, the U.S. Court of Appeals for the Sixth Circuit removed Kentucky attorney Steven N. Howe from representing appellant John C. Farris. United States v. Farris, No. 25‑5623, (6th Cir. Apr. 3, 2026) (slip op. at 6) ("by separate order issued on this same date, we remove Howe from further representation of Farris"). The court found that Howe had "committed inexcusable transgressions during the appellate phase of this case" by filing appellate briefs generated with Westlaw's CoCounsel platform. By Howe's own admission, he directed staff "to upload district court documents to Westlaw's CoCounsel program to create a first draft" of both briefs, without "properly verifying the cited legal authorities," which resulted in "multiple misrepresentations of law to this Court," including "three inaccurate quotations" that "do not appear in any legal authorities" -- entirely misrepresenting the holdings of the cited cases. Id. at 1-4, 6. While acknowledging that "new technologies present significant promise for the legal field," the panel cautioned that "all in the legal profession must be clear eyed about technology's potential pitfalls . . . ." Id. at 6. Moreover, they emphasized that "attorneys who rely on artificial intelligence must remain diligent in supervising their work product and carefully examine the accuracy of every citation they present to this Court," criticizing Howe's reliance on "staff" -- rather than himself or another attorney -- to supervise the AI‑generated work product as falling "short of his obligations as attorney of record." Id. at 5. The court further emphasized that "attorneys who choose to use artificial‑intelligence tools must do so in a manner consistent with their ethical obligations," and that relevant steps "may include reviewing and validating content produced by artificial intelligence; considering whether to disclose the use of artificial intelligence to clients or obtain informed consent; safeguarding confidential client information and preserving attorney‑client privilege; implementing firm‑wide policies governing the use of artificial intelligence; adhering to ethical billing practices when using artificial‑intelligence tools; and keeping current with jurisdiction‑specific guidelines." Id. at 5.

In Howe's case, the court found that he "failed to adequately review and verify the draft brief produced by artificial intelligence" and that his "failure to verify the artificial‑intelligence output still resulted in the submission of false quotations and misleading legal arguments to this Court," which "necessitated a significant use of judicial resources to investigate the suspected artificial‑intelligence improprieties, coordinate a response, and facilitate additional steps of these appellate proceedings." Id. at 4-6. The panel concluded that "attorneys have an ethical obligation to verify the citations and propositions they submit to courts; that obligation reflects duties of competence and candor that apply no matter the tools attorneys use," and that "new technologies, moreover, are no substitute for tried‑and‑true safeguards managed by practicing attorneys," underscoring that technological convenience cannot replace traditional professional judgment and supervision. Id. at 5.

In a recent French proceeding, the Tribunal Judiciaire of Périgueux formally identified legal submissions relying on "untraceable or erroneous" precedents, and on December 18, 2025, expressly noted that the claimant's arguments contained fabricated authorities. See Tribunal Judiciaire de Périgueux, December 18, 2025, n° 23/00452. Comparable failures have arisen in other common law jurisdictions, too. For example, in a damages action for £89.4 million brought by Hamad Al‑Haroun against Qatar National Bank QPSC and QNB Capital LLC in the High Court of Justice, King's Bench Division, the claimant's solicitor placed before the court a schedule of 45 case citations; in 18 instances, the cited case did not exist, and among the authorities that did exist, many "did not contain the quotations that were attributed to them, did not support the propositions for which they were cited, and did not have any relevance to the subject matter of the application . . . ." R (Ayinde) v. London Borough of Haringey; Al‑Haroun v. Qatar Nat'l Bank QPSC & QNB Capital LLC, [2025] EWHC 1383 (Admin) [73]-[74] (Eng.) In its June 6, 2025, Divisional Court judgment, the court concluded that "[t]he vast majority of the authorities are made up or misunderstood." Id. at ¶ 74. The High Court warned that "[t]here are serious implications for the administration of justice and public confidence in the justice system if artificial intelligence is misused" and emphasized that "practical and effective measures must now be taken by those within the legal profession with individual leadership responsibilities (such as heads of chambers and managing partners) and by those with the responsibility for regulating the provision of legal services . . . ." Id. at ¶ 9. Moreover, those measures must ensure that "every individual currently providing legal services within this jurisdiction (whenever and wherever they were qualified to do so) understands and complies with their professional and ethical obligations and their duties to the court if using artificial intelligence." Id. at ¶ 9.

Concerns extend beyond just hallucinations to confidentiality and data security. Arbitration proceedings routinely involve sensitive commercial information subject to strict confidentiality obligations. Because of the learning process utilized by generative AI, inputting such material into publicly accessible generative AI platforms risks unauthorized disclosure and downstream use beyond the parties' control. For this reason, limited AI use in arbitration should be confined to secure, private environments and accompanied by verification protocols equivalent to those applied to human work product. When deployed within these boundaries, AI can enhance efficiency and speed without undermining accuracy, professional responsibility, or confidence in the arbitral process and the enforceability of the resulting award.

II. Medium-Scale AI Applications: Data-Driven Arbitrator Selection

At the medium range of application, AI moves beyond administrative support and into a procedurally consequential role: arbitrator selection. Trust in the arbitrator is central to the legitimacy of arbitration. Parties expect decision-makers who are competent, independent, diligent, and attuned to the issues that matter most in a given dispute. Traditionally, the selection process has relied on professional networks, institutional rosters, published awards, and informal reputational knowledge. Increasingly, however, that process is being reshaped by data-driven and AI-enabled tools that aggregate, analyze, and systematize information about arbitrators' backgrounds, experience, procedural tendencies, and conflicts.

This development fits naturally within existing arbitral frameworks. Because arbitration is a creature of contract, the method of selecting arbitrators is dictated by party agreement. In ad hoc proceedings, parties must identify mutually acceptable candidates on their own. In administered arbitrations, institutions play a central role in this determination. Under the rules of the International Chamber of Commerce, the Court appoints arbitrators when parties cannot agree. See, 2021 Arbitration Rules - ICC - International Chamber of Commerce. By contrast, the American Arbitration Association and its international division, the International Centre for Dispute Resolution, rely on a list method. Based on party input, the institution generates a list of potential candidates drawn from its roster. It then provides résumés, conducts conflict checks, and ultimately appoints arbitrators based on party rankings. AI tools are now being introduced to assist with these functions by scanning résumés, identifying relevant expertise through keyword mapping, assessing availability and caseload, and flagging potential conflicts more efficiently than traditional manual review. See, 2026_ICDR_Arbitrator_Selection_Services.pdf. In this configuration, AI acts as a sophisticated filter and organizer, but human case administrators and parties remain responsible for interpreting the results and making the final selection.

For parties and counsel, AI-enabled arbitrator selection tools promise several advantages. Platforms that aggregate publicly available awards, enforcement decisions, professional writings, and institutional data can cast a wider net of potential candidates, including lesser-known or more diverse arbitrators who may not surface through informal networks. Tools developed by providers such as Jus Mundi, including its professional directory and conflict-mapping features, use extractive AI to organize arbitrators' experience by industry, issue area, language, and procedural role. See, Jus Connect | The professional network for arbitration. Institutions have followed suit. In 2024, the AAA-ICDR announced the beta launch of a generative AI-powered panelist search tool designed to broaden and deepen roster searches when preparing arbitrator lists. See, AAA-ICDR Launches AI Tool for Panelist Selection.

At this intermediate level, AI does not make the appointment decision. Humans remain in the loop and discern between varying options. But AI meaningfully shapes the pool from which choices are made. At the same time, medium-scale use of AI in arbitrator selection raises distinct concerns. International commercial arbitration is largely confidential. Awards are often unpublished, heavily redacted, or unreasoned. Even where institutions publish awards, such as the ICC's collaboration to publish selected awards through Jus Mundi, the available data is partial and curated. In three-member tribunals, outcomes may reflect compromise rather than the views of any single arbitrator, and dissenting opinions remain rare. As a result, the datasets on which AI tools rely are incomplete and, in some cases, misleading. Thus, predictive insights, drawn from such data, risk overstating precision and attributing philosophies or tendencies that cannot be reliably inferred.

Bias presents an additional challenge. AI systems trained on historical arbitrator data may reinforce existing patterns of repeat appointments and underrepresentation. International arbitration has long been criticized for a lack of diversity, particularly along gender, racial, and regional lines. Because historical data disproportionately reflects appointments of senior, Western, male arbitrators, AI models trained on that data risk perpetuating the same imbalance. Efforts to promote diversity, such as the ICDR's stated diversity targets, may be undermined if AI outputs are treated as neutral or objective without scrutiny. See, Diversity | International Centre for Dispute Resolution. Moreover, many diversity-related attributes are not captured in professional biographies or résumés, limiting the ability of AI systems to account for them in any meaningful way.

Accordingly, medium-scale AI applications in arbitrator selection demand careful calibration. AI can improve efficiency, expand visibility, and systematize conflict analysis. It can assist both institutions and parties in managing increasingly complex datasets. But it cannot replace judgment, contextual understanding, or ethical responsibility. Decisions about who should decide a dispute remain normative, not purely technical. At this level of use, the promise of AI lies in informed assistance, not automation, and its legitimacy depends on transparency, critical interrogation of outputs, and continued human responsibility for the ultimate appointment decision. In other words, the technology may curate and analyze, but humans must still choose, thereby remaining in the loop.

III. Large-Scale Application: Can AI Be the Decision Maker?

At the far end of the spectrum, AI is no longer confined to support or select functions, but is embedded directly into the adjudicative process itself.

The most prominent example to date is the AI-led arbitration initiative launched by the American Arbitration Association, International Centre for Dispute Resolution (AAA-ICDR). See, AI Arbitrator, Fast and Fair Dispute Resolution by AAA. An "AI arbitrator," in this context, does not mean a system that independently decides disputes. Rather, it refers to an AI-enabled adjudicative framework designed to assist human arbitrators in delivering faster, more cost-effective, and transparent outcomes in narrowly defined cases, while preserving human judgment at every decisive stage. The AAA-ICDR AI arbitrator was developed for two-party, document-only construction disputes -- a category of cases where claims, evidence, and legal issues are often structured and repeatable. Id. Its development relied on a curated legal and factual knowledge base created by AAA-ICDR attorneys and dispute-resolution experts, who annotated more than 1,000 prior construction cases. These materials include arguments, evidence, outcomes, and structured reasoning frameworks, forming a high-quality dataset intended to reflect how experienced arbitrators analyze claims rather than how a generic language model predicts text. Id. To operationalize this expertise, AAA-ICDR partnered with QuantumBlack, an advanced analytics and AI firm, which worked alongside arbitrators and industry specialists to codify arbitral reasoning into prompts and agents. Id. AAA Construction Panel arbitrators and AAA-ICDR attorneys were involved from the outset, ensuring that the system follows real arbitration logic step by step, rather than abstract or purely statistical inference. Id. Under the AAA-ICDR AI-Led Arbitration Rules, the process is structured and transparent; parties submit their claims and supporting evidence through the platform; the AI system produces summaries of the submissions, which the parties must validate for accuracy; the AI then analyzes the claims, reviews the evidence, applies the relevant law, and drafts a proposed award. Id.

Crucially, that draft never becomes final on its own. A human arbitrator, trained specifically for AI-led cases and vetted through the same institutional processes as any other AAA appointment, reviews the AI's analysis, has access to the full record, makes any necessary revisions, and issues the final, binding award. Id. Every AI-led case therefore involves human adjudication at the decisive moment. The system is entirely opt-in, requiring the express consent of both parties; absent agreement, the dispute proceeds under traditional AAA arbitration. Id. Accordingly, early use, indicates cost savings in the range of approximately 35-45% compared to traditional documents-only arbitration, with time savings of roughly 20-25%. Id. Despite these safeguards, large-scale AI use raises concerns distinct from those associated with smaller applications. Practitioners and clients may question whether reliance on AI, however supervised, affects perceptions of neutrality, due process, or legitimacy. Others may worry about transparency in reasoning, the scope of disclosure obligations, or whether the use of AI should be affirmatively disclosed to preserve enforceability.

These concerns are not merely theoretical. They intersect directly with how courts and enforcement bodies may view awards produced through AI-assisted processes, particularly across jurisdictions with differing regulatory approaches to automated decision-making. In this respect, the EU's approach under the EU Artificial Intelligence Act is instructive. See, The AI Act Explorer | EU Artificial Intelligence Act. The regulation emphasizes that individuals should not be subject to decisions producing legal effects, or similarly significant consequences, where those decisions are based solely on automated processing. Article 14 requires meaningful human oversight, ensuring that automated systems do not operate autonomously in legally binding decision-making. See, Article 14: Human Oversight | EU Artificial Intelligence Act. Automated decision-making is permitted only in limited circumstances, such as necessity for contract performance, express legal authorization, or explicit consent, and even then, must be accompanied by safeguards, including transparency about the logic involved, the right to human intervention, and the ability to contest the outcome. Id.

Although arbitration is contractual and party-driven, these principles underscore a broader regulatory sensitivity to systems that might be perceived as replacing human judgment in legally consequential decisions. Against that backdrop, the AAA-ICDR model is designed to align with emerging regulatory and enforcement expectations. Human legal judgment remains central, and the arbitrator issuing the award is identifiable, accountable, and subject to the same ethical obligations, disclosure duties, and institutional oversight as in any other AAA proceeding. A dedicated AI Governance Committee oversees compliance, ethics, and model performance.

Whether national courts will treat AI-assisted awards differently at the enforcement or vacatur stage remains an open question. What is clear, however, is that large-scale AI use in arbitration will be scrutinized through the lens of consent, transparency, human oversight, and procedural fairness. In that sense, AI-led arbitration does not eliminate traditional concerns about legitimacy, it reframes them, placing renewed emphasis on how technology is integrated, supervised, and disclosed within the arbitral process. Future models that move closer to fully automated adjudication are likely to collide with these requirements unless they retain a clearly identified human in the loop capable of explaining, revising, and ultimately owning the decision.

IV. A Human in the loop, the future of AI in Arbitration

The phrase "human in the loop" began as a technical way to describe a person inside a system's chain of action and response.[2] The "loop" is the cycle: information comes in, a decision is made, something happens, and the result feeds back into the next decision. The term stuck because it captures a powerful idea: the human is not merely watching the machine, but sensing, judging, correcting, and sometimes overruling it. Today, in automation and AI, it points to a larger question: where should human judgment still enter when machines can act on their own? The question is no longer whether artificial intelligence will play a role in arbitration, but how far that role will extend.

At the limited end of the spectrum, AI tools that assist with document organization, bundle preparation, and legal research are already embedded in arbitral practice. At an intermediate level, data-driven tools are reshaping arbitrator selection by aggregating experience, mapping conflicts, and expanding visibility beyond familiar networks. At the most ambitious end, initiatives such as the AAA-ICDR's AI-led arbitration framework demonstrate that institutions are prepared to experiment with AI at the adjudicative core of the process. Taken together, these developments suggest that AI-enabled arbitrator tools are likely to become a permanent feature of institutional arbitration. If that is the direction of travel, the arbitration community must be prepared. Preparation, in this context, means not only learning how to use AI tools, but also articulating where human control, responsibility, and explanation must remain non-negotiable.

Those preparations begin with restraint. The efficiency gains offered by AI, speed, cost reduction, and analytical support, are real, but they do not diminish the need for diligence, verification, and professional judgment. The documented incidents of hallucinated authorities, and sanctioned filings, underscore that even limited AI use can undermine legal credibility, if outputs are treated as authoritative rather than provisional. In arbitration, where confidentiality and accuracy are paramount, AI must be approached as an assistive technology, not a substitute for human responsibility. As AI assumes a more structural role, particularly in arbitrator selection, its influence becomes normative as well as practical. Data-driven tools can broaden candidate pools and support more informed appointments, but they also operate on incomplete and historically skewed datasets. Without careful governance, they risk reinforcing repeat appointments and existing disparities rather than mitigating them. Transparency about data sources, critical interrogation of outputs, and continued human discretion, remain essential to preserving party autonomy, and confidence in the process. These are all dimensions of a human-in-the-loop architecture, in which people remain answerable for how AI is used and for the choices it ultimately informs.

Large-scale applications, such as AI-led arbitration, bring these issues into sharp relief. Models that embed AI in the reasoning process, while preserving human oversight, party consent, and institutional accountability, reflect an effort to reconcile innovation with due process. Yet they also invite scrutiny at the enforcement stage, particularly in light of emerging regulatory frameworks like the EU Artificial Intelligence Act that emphasize meaningful human oversight and limit legally binding decisions based solely on automated processing. The enforceability of AI-assisted awards will ultimately turn not on the presence of AI, but on whether human judgment, transparency, and procedural fairness remain demonstrably intact. If major arbitral institutions do move toward widespread use of AI arbitrator tools, the legitimacy of arbitration will depend less on technological sophistication than on governance.

Clear procedural rules, informed consent, disclosure where appropriate, robust oversight, and professional education will determine whether AI strengthens arbitration's core values or strains them. Arbitration has always evolved through adaptation. The challenge is to close the loop now to ensure that, as AI becomes part of the arbitral architecture, it is integrated in a way that enhances efficiency without compromising fairness, enforceability, or trust in the arbitral process. Put differently, the sustainable future of AI in arbitration is one in which humans remain visibly, accountably, and meaningfully in the loop.

[1] See, e.g., Cole Stryker, What is human-in-the-loop?, IBM.com, https://www.ibm.com/think/topics/human-in-the-loop.

[2] See Cole Stryker, What is human-in-the-loop?, IBM.com, https://www.ibm.com/think/topics/human-in-the-loop; Ge Wang, Humans in the Loop: The Design of Interactive AI Systems, hai.standford.edu (October 19, 2019), https://hai.stanford.edu/news/humans-loop-design-interactive-ai-systems; Kim Herrington, Be THE Human In The Loop: Data And AI Literacy Is Your Edge, Forrester.com (March 27, 2025), https://www.forrester.com/blogs/be-the-human-in-the-loop-data-ai-literacy-is-your-edge/. more

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Career development 3d

I have been unemployed for two years. I've been searcing for work the whole time and only got two interviews. Both interviews went badly. I have lost confidence in my self and skills. How can I best keep my mind sharp and improve job seeking results
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  • Hariel Corsair

    23h

    Wow. 2 years. Memories of Covid were still fresh back then. But opportunities and additional ways of building income were sprouting up all over as... well. Zoom became the new office! hahaha
    It also became easier to get in rooms (like this) and surround yourself with positive, forward-thinking people nationwide. I found it more enjoyable and financially profitable to work for myself! Don't give up on yourself. Just surround yourself with folks who will believe in you, more than you believe in yourself. :-)     more

  • andrew wedeck

    2d

    I have been there done that. I also lost my confidence @ one point. In retrospect it was completely silly, completely in my head. I do not know you... but would guess like most people you have some very good skills and character traits. Start with the fact that you took the initiative to reach out on this web site for ideas. Being proactive is a wonderful trait. Believe in yourself and find people who believe in you.  more

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Career development

 1d

Advancing Your Leadership Journey: Career Development Goals For Leaders


As a leader, it is crucial to continuously strive for growth and improvement in your professional journey Setting career development goals can help you stay focused, motivated, and on track towards achieving your ultimate leadership potential Whether you are a seasoned executive or a new manager, here are some key career development goals to consider as you navigate your leadership path.

1... Cultivate Emotional Intelligence:

Emotional intelligence is a critical trait for effective leadership Leaders with high emotional intelligence are better equipped to navigate complex interpersonal relationships, communicate effectively, and inspire trust and confidence in their teams To cultivate emotional intelligence, consider strategies such as practicing active listening, developing empathy, and seeking feedback from others to gain self-awareness and insight into your own emotions and behaviors.

2 Enhance Communication Skills:

Effective communication is at the core of successful leadership Leaders must be able to articulate their vision, goals, and expectations clearly and concisely to their team members To enhance your communication skills, consider participating in public speaking workshops, taking courses on effective communication techniques, or seeking out opportunities to practice and receive feedback on your communication style.

3 Develop Strategic Thinking:

Strategic thinking is the ability to anticipate future trends, identify opportunities and threats, and develop proactive plans to achieve long-term goals Leaders who possess strong strategic thinking skills are better positioned to lead their organizations through change and uncertainty To develop your strategic thinking abilities, consider studying industry trends, engaging in scenario planning exercises, and collaborating with peers to brainstorm innovative solutions to complex challenges.

4 Foster a Culture of Innovation:

Innovation is the key to driving growth and staying competitive in today's rapidly evolving business environment Leaders who foster a culture of innovation within their organizations are able to inspire creativity, experimentation, and continuous improvement among their teams career development goals for leaders. To foster a culture of innovation, consider creating opportunities for brainstorming and collaboration, recognizing and rewarding creative thinking, and encouraging a spirit of curiosity and openness to new ideas.

5 Invest in Lifelong Learning:

Leadership is a journey of continuous growth and development To stay ahead in your career, it is essential to invest in lifelong learning and skill development Whether through formal education, professional certifications, or on-the-job training opportunities, committing to ongoing learning can help you stay current with industry trends, expand your knowledge base, and enhance your leadership capabilities.

6 Build a Strong Personal Brand:

Your personal brand is a reflection of your values, strengths, and unique leadership style Building a strong personal brand can help you stand out in a competitive job market, attract opportunities for career advancement, and establish credibility and trust with your peers and colleagues To build a strong personal brand, consider defining your core values and passions, cultivating a strong online presence through social media and networking platforms, and seeking out opportunities to showcase your expertise and leadership accomplishments.

7 Mentor and Develop Others:

As a leader, one of your most important responsibilities is to mentor and develop the next generation of leaders By investing in the growth and development of your team members, you can create a pipeline of talent, build a culture of continuous learning, and leave a lasting impact on the future success of your organization To mentor and develop others, consider providing coaching and feedback, creating opportunities for stretch assignments and professional growth, and serving as a role model and mentor to aspiring leaders within your organization.

By setting achievable career development goals and committing to continuous growth and improvement, you can advance your leadership journey, inspire those around you, and achieve your full potential as a leader Whether you are focusing on developing emotional intelligence, enhancing communication skills, fostering a culture of innovation, or investing in lifelong learning, there are countless opportunities for growth and development on your leadership path Embrace the challenge, stay humble, and keep pushing yourself to reach new heights in your leadership journey. more

Career development

 1d

Why AI interviews are losing 1 in 3 candidates


Both employers and job seekers are increasingly tapping AI to guide the job interview process, but their strategies are still very much a work in progress.

New research finds real hesitation among job candidates about interviewing for a job with no human present, with concern spiking when transparency isn't centered in the interview process. At the same time, they are increasingly leveraging AI... for real-time assistance during live interviews, highlighting the complexities facing today's HR and recruiting professionals in an AI-influenced job market.

On the employer side, a report from Greenhouse found that AI job interviewing is rapidly surging: In a survey of nearly 3,000 job seekers, nearly two-thirds have experienced an AI interview, a 13-point jump from last year.

But despite the increased frequency of AI interviews, the reviews aren't positive. Nearly 40% said they have stopped job interviews that were being conducted by AI, with another 12% saying they would do so. What prompted candidates to hit the X button? The most common reason was the realization the interview was a pre-recorded, AI-generated video with no human present, closely followed by unclear disclosure by the company of AI's involvement in the process.

Transparency is a big factor: Seventy percent of applicants said the employer didn't clearly disclose that AI would be involved in the interview process; nearly a quarter only found out once the interview started. Yet, candidates want employers to be more upfront. More than half think employers should be legally required to disclose how they're using AI in interviewing.

"Candidates are telling us exactly what they want, and it isn't complicated: Tell them when AI is in the room and what it's measuring," says Sharawn Tipton, Chief People Officer at Greenhouse. "Right now, most employers are failing that test."

When candidates drop out of the interview process because of AI use, it's not just one missed hire, Tipton notes. It could also fuel an ongoing reputation problem.

"Until we get honest about what these tools are actually measuring and own it when they get it wrong," she says, "we're just repackaging the same problem."

How job candidates are using AI

That's not to say that candidates don't want AI to play a role in interviewing. Greenhouse's research found that just 19% want employers to decrease AI use in hiring. Instead, they want the opportunity to request to speak with a human, and more transparency about how the AI is being used and that a human will be in the loop.

Those findings pair with a recent report on candidate AI use from Resume Genius. More than three-quarters of interviewed job seekers said they have used or are open to using AI in their job application process. And 22% have utilized AI during a live interview to help them respond to interviewer questions, with nearly as many using the tech to complete skills tests.

"What started as a preparation tool," researchers say, "is now showing up during the interview itself and even in the evaluations meant to measure ability."

Just as employers need to emphasize transparency around their own use of AI in interviewing, they need to be clear upfront about expectations for candidate AI use, says Eva Chan, career expert at Resume Genius.

"When a tool can easily provide you with faster answers than Google or your own brain, it makes sense that people would start turning to it beyond just for writing their resumes," Chan says. "But as usage moves into interviews and assessments, employers need to decide where they stand -- and communicate that stance clearly before the process begins."

For many job seekers, AI has moved from curiosity to consideration. What once felt experimental is becoming more accepted as candidates look for new ways to keep up in a competitive market. more

Maria Peres


Career development 3w
I work as a waitress in a small restaurant. When another waitress job opened, I suggested hiring my 19-year-old daughter. She needs a part-time job while studying. But the manager said no. He thinks we might be dishonest and cheat him. I don’t understand how we could do that or why it’s a problem when my daughter works with me. Can you explain?

  • Joe Kamanje

    1d

    Actually he admired your daughter

  • Sheila Milburn

    2w

    Its understandable. Even without theft or collusion.
    If you need leave due to a family emergency chances are she'll need off also. If one of you... gets fired. Will the other quit. If one gets the flu does the other get it. There are too many chances of double absences. Family vacations, events, etc could lead to staffing problems.  more

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Career development

 1d

Who am I?


"Tell me about yourself -- who are you, where are you from?"

A line all too familiar. One we hear throughout our lives, in classrooms, job interviews, first dates and in new rooms full of strangers. It almost always makes us nervous, doesn't it? I've introduced myself in every way I could think of -- by my name, my job, my hometown, my interests -- and yet, the question never quite feels... settled.

Who am I?

I used to think I simply hadn't found the right words yet. That somewhere out there was a perfect, complete answer waiting for me to discover. But I'm starting to think the question was never meant to be answered cleanly. I've learned over time that who we are today is different from who we were yesterday and who we'll be tomorrow. Change, experiences, decisions, losses -- they all shape our being in ways we rarely see coming.

I learned this most recently not from a book or a conversation, but from a eulogy.

My grandfather passed away a few days ago. Today was his burial. I live in Barcelona now, far from where my family gathered in the Philippines, so I joined it the only way I could: through a video call. During his funeral, his son -- my uncle, stood up and did what we ask of people in their worst moments of grief: he tried to summarize a man. He spoke about who my grandfather was, the life he lived, the person he had been to all of us in that room. And I watched alone at my apartment, groggy, crying at 2 in the morning on a screen, just as I've expected. What I didn't expect was the specific ache underneath those tears -- this quiet, striking realization that we had all been living alongside this man for years, and only now, in his absence, were we attempting to fully see him.

Life, I was reminded, is deeply intangible. The distance makes it all even sharper. Life was happening -- real, final, irreversible and I was watching it through this little rectangle of light in the darkness.

We move through life assuming there is always time -- time to understand the people we love, time to understand ourselves. And then something stops, and suddenly, we're in a room listening to someone piece together a person from their memory, and it hits you how much always goes unsaid. How incomplete every introduction really is.

So here I am, doing the uncomfortable, slightly terrifying thing of introducing myself, again, but this time on a page, to strangers, on my own terms. Not because I finally have a clean answer to who I am. But because I've realized that waiting for one means waiting forever.

This is my first post. I don't know who I'll be by the last one. Heck, I don't even know if I can get through the next one. But I think that's exactly the whole point.

I am still becoming. more

Career development

 1d

Alma Mater Homecomings: Celebrating Shared History and Enduring Bonds


As our alma maters host homecoming events, it is a time for alumni to reflect not only on the path they have traveled but also on how they present themselves within these familiar halls. While the joy of reconnecting is paramount, a subtle yet significant aspect often goes unaddressed: the art of attending without offense or ostentation.

The fundamental truth of any alumni reunion is simple: It... is a reunion, not a résumé contest. We are there to share memories, reminisce about laughter and lessons, and reflect on the experiences that shaped us. Our presence is a testament to gratitude for the foundation our schools provided, not a showcase of our accumulated net worth or professional accolades.

Adhering to basic alumni etiquette ensures a harmonious experience for all. Punctuality and active participation, whether for a solemn Mass or an engaging program, demonstrate respect for organizers, former teachers, and mentors. Following the dress code is not about appearance but unity and acknowledgment of their planning.

Warm greetings extend a powerful sense of inclusivity. During speeches, especially from jubilarians or educators, mindful attention and putting phones away show respect for and honor the speaker's contribution.

The spirit of sharing, rather than dominating, is crucial. While recounting our journeys is natural, it is important to engage others and allow quieter batchmates to share their narratives. Responsible consumption of beverages is paramount; respecting the program's flow and refraining from side conversations during awards or tributes, ensures every moment is appreciated.

Conversely, common pitfalls can unintentionally cause offense. The most pervasive is flexing job titles, possessions, or travel experiences. A casual mention of a recent European trip is understandable, but repeating it can alienate others. Alumni events are not fertile ground for sales pitches or multilevel marketing schemes. Attendees come to reminisce and reconnect, not to be solicited for business.

The microphone and dance floor, too, have their limits. Batch presentations should be concise and respectful of allotted time. Dredging up embarrassing past incidents, such as academic failures or unrequited romantic interests, can reopen old wounds and cast a pall over the reunion.

A key point is unwavering respect for teachers and staff, the architects of the memories we are celebrating. Taking time to greet them, express gratitude, and perhaps even pose for a photograph is a small act with immense sentimental value. Avoiding cliquish behavior is also vital; while it is natural to gravitate toward close friends, making an effort to mingle across different batches fosters a broader sense of belonging.

The political arena should remain outside reunion conversations. Homecoming is a time for unity and shared history, not divisive debates or arguments. In the age of social media, discretion with photos is essential. Asking permission before tagging individuals, especially in unflattering pictures, ensures everyone's comfort and privacy.

The golden rule, deeply ingrained in our Filipino culture, aptly encapsulates the ideal approach: "Walang lamangan, walang yabang, walang iwanan." This translates to celebrating success without arrogance and ensuring no one is left behind. When sharing personal achievements, framing them with gratitude is key. Instead of boasting about becoming a CEO, a more appropriate sentiment would be, "I am blessed to be where I am today, and I attribute much of that to the foundation this school and these teachers provided."

Alma mater homecomings reinforce the bonds that tie us to our past and to each other. By embracing humility, respect, and a genuine desire to connect, we can ensure these events remain vibrant celebrations of shared history and enduring community.

REGINALD B. TAMAYO,

Marikina City

For letters to the editor and contributed articles, email to [email protected] more