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Can I just be mentored?

Posted by [email protected] on 09/09/2026 3:19 pm  

Can I just be mentored?

By Jackie Kohlhepp, SHRM-SCP, LISW-AP

Jaclyn “Jackie Kohlhepp” is the Founder & CEO of The Rez Rev, LLC and President of SHRM Charleston. She is a workforce strategist, entrepreneur, and talent development leader focused on connecting employers and emerging talent.

There's a quote from the movie 10 Things I Hate About You (are any of my fellow Millennials feeling nostalgic?) where Chastity, turns to Bianca and poses the ever-so-deep philosophical question: "I know you can be overwhelmed, and you can be underwhelmed, but can you ever just be whelmed?"

While I can't answer that timeless question, it did inspire one of my own: Can you ever just be mentored?

We hear a lot about professionals, particularly women, being over-mentored and under-sponsored. Research shows that while many professionals receive career advice and feedback a plenty, they're often less likely to have influential leaders actively advocate for their growth within the organization.

Sponsorship is important. Yet, we don’t want to overlook another question:

Have we forgotten what good mentorship looks like?

In a world where the word “mentorship” is tossed around like a bunch of business cards at a networking event, it’s easy to lose sight of what makes mentorship so meaningful. I believe it is possible to “just be mentored” and when it’s done well, powerful relationships form that lead to long-lasting growth for us and for the environments we impact. 

Before we explore what real mentorship is, we’ll begin by exploring what it isn’t.

Up first, overmentorship. Have you ever found yourself receiving more guidance than bargained for from a mentor? I have. So much so that I found myself feeling limited in my ability to grow and less eager to seek feedback or advice.

Overmentorship is making assumptions about the level of support someone needs or doesn’t need without actually knowing what will be most helpful to develop their professional abilities and goals. In other words, it’s more about giving back than moving someone forward. 

In my case, someone with more experience generously offered to help me navigate the next steps in my career. The result? Rather than feeling empowered, their approach left me feeling like I needed permission to take my next steps. The intention was well-meant. The execution missed the mark.

Next, undermentorship. This mentorship pitfall occurs when a mentee doesn’t receive enough guidance to grow from their successes and mistakes. When others at the organization might have more access to coaching, support, and feedback, the individual may not have the same access. It’s expecting without equipping someone to succeed.

I have also witnessed firsthand what this sort of relationship looks like. In my case, the mentor missed an opportunity to establish clear guidelines and consistency in the relationship, leading to underdevelopment for the mentee. The mentee confided in me, “I feel like I’m in this alone”.   

Individuals do not necessarily decide they want to “undermentor” someone. Sometimes, it occurs due to a lack of mentorship know-how, a lack of time, or even lack of resources. 

Having experienced and observed under and overmentorship, I can appreciate true honest to goodness mentorship. The kind that elevates both the mentee AND the mentor. 

Let’s define good mentorship: 

  • A mutually beneficial relationship built on trust: Real mentorship happens when the mentee and the mentor are clear on the expectations, understand the benefits, and commit to a plan that is mutually impactful.
  • An emphasis on counsel and feedback: Great mentors serve as guides to help mentees learn from their accomplishments and their challenges. It’s a developmental relationship and often reciprocal with the mentor occasionally finding themselves on the receiving end of knowledge and lessons learned, referred to as reverse mentoring. 
  • A balance between cozy coaching and challenging conversations: Strong mentorship happens when the mentor and mentee not only discuss what’s going well but broach topics that the mentee may find professionally challenging or tough to discuss. We often grow during those uncomfortable moments and great mentors make those conversations possible safely. 

This brings us to our next question: How to be a good mentor?

·        Set clear boundaries and define expectations. Both the mentor and mentee should be aligned on the need for mentorship and the purpose. Both should be able to answer: What will change in my career or professional life as a result of this relationship?

·        Create mutual guardrails. The mentor and mentee should be committed to the mentoring relationship by setting consistent meetings or related activities (e.g., role-playing, preparing for opportunities, reviewing professional materials, making introductions, etc.), and creating a plan for what will happen during those meetings. 

·        Build in sponsorship opportunities. Great mentorship makes a pathway for advancement. Mentors should consider how they can incrementally build connections or stretch opportunities for the mentee. 

For a mentorship relationship to be a success, both parties need to have the right mindset. This includes- 

·        Commitment to learning. Both the mentor and mentee must be open to learning from each other, admitting that they don’t know everything, and being honest when things do not go as planned. 

·        Reciprocal trust. The most powerful mentorship occurs when both parties can take risks (e.g., being vulnerable or asking challenging questions). It’s through that risk-taking that the most powerful growth happens. People do not take risks if there is no trust. Trust is built and grows over time. 

Your next step. 

If you are currently or are considering mentoring someone, take a moment to self-assess- where do you fall on the mentoring spectrum? Are you creating conditions that lead to true growth or are you just giving advice? Next, ask yourself: What can I do today to make this relationship more meaningful, challenging, and supportive? 

 

 

AI in HR: Automation Doesn’t Eliminate Accountability

Posted by [email protected] on 09/03/2026 3:59 pm  

AI in HR: Automation Doesn’t Eliminate Accountability

By Lindsey Taylor, SHRM-CP

Lindsey Taylor is a Senior Consultant with Maly Consulting specializing in equal employment opportunity compliance and workforce analytics. For more than a decade, she has helped employers analyze their workforce data and employment practices for potential disparities and bias to support fair and compliant workplaces.


Only a few years ago, conversations around AI in HR largely focused on efficiency and automation. Today, research and litigation are shifting another consideration to the forefront: fairness. What happens when the tools designed to help employers make faster decisions also influence who gets considered, who gets selected, and who gets left behind?

It is easy to understand why HR has embraced AI. According to 2025 SHRM research, about 66% of surveyed HR professionals report using AI daily at work, and 89% of HR professionals who use AI in recruiting say it saves time or increases efficiency. 

AI tools also seem to offer something particularly appealing in employment decisions: objectivity. An algorithm, after all, does not have instincts, personal preferences, or unconscious bias in the same way humans do. But that does not necessarily make its decisions neutral. 

AI Can Learn Bias, Too
A study from Princeton and the University of Chicago put this idea to the test. Researchers used large language models (LLMs), including ChatGPT, Claude, and Gemini, in hiring simulations to examine how biases can develop. As the models received feedback about fictional employees’ performance, they began creating their own stereotypes about fictional ethnic groups. 

For example, after a model was told a member of one ethnic group performed poorly as a doctor, it began avoiding other people from that same group for doctor positions and instead hired them for jobs like janitors. Overall, the AI models showed significantly more occupational segregation than humans, with segregation scores about 65% higher on average.

That matters because AI does not need to be intentionally programmed to discriminate. LLMs are designed to identify patterns and make generalizations from limited data, so as they learn from outcomes, they may also learn patterns we never intended them to learn.

And this concern is not limited to a laboratory. In a 2026 study, Stanford researchers examined real-world data from more than four million job applications across 150 employers for evidence of adverse impact—whether a seemingly neutral practice disadvantages a group. They found that 26% of Black applicants and 15% of Asian applicants had applied to positions where the AI system disadvantaged their racial group. Researchers estimated that roughly 40,000 applications may have advanced in the hiring process if selection rates had been equal across groups.

How adverse impact is measured matters. When the researchers combined results across employers and positions, the system appeared to show no adverse impact. But when they analyzed individual positions, they uncovered the differences in selection rates. An AI vendor’s overall bias analysis may therefore miss disparities within specific positions. Employers should understand how a tool has been tested for bias and monitor whether disparities emerge within their own positions.

The researchers also identified the potential for what they called “algorithmic monoculture.” Job seekers who applied to multiple positions screened by the same AI hiring platform were more likely to be rejected from all of them than if companies made decisions independently from one another. This can happen when employers rely on a common vendor whose models evaluate applicants similarly. In other words, when a single hiring vendor is used by many companies, some applicants may be shut out of jobs.


AI Bias in the Courts
The repercussions of potential AI bias are beginning to play out in the courts. 

There is the closely watched class action lawsuit against Workday, which challenges whether its AI-powered hiring tools discriminate against applicants based on race, age, and disability. The plaintiff, a Black man over 40 with a disability, claims he was rejected from more than 100 roles, sometimes within minutes of applying, despite being qualified. 

The legal concerns around AI also extend beyond hiring. A federal lawsuit against Meta claims that AI-assisted systems used in the company’s layoff process disproportionately targeted employees on protected medical, parental, or disability leave. According to the lawsuit, these systems evaluated workers’ productivity levels without adequately accounting for time spent on protected leave. This allegedly contributed to their layoff scores and potentially violated several federal and state employment laws. 

These cases are still developing, but they raise an important question for employers: If an AI system helps make an employment decision, who is responsible for making sure that decision is fair?

Automation Doesn’t Eliminate Accountability
AI is not inherently bad. It can be incredibly valuable for HR professionals. But it’s important to keep in mind AI’s limitations. AI tools learn from historical data, so if that data reflects existing disparities, AI may copy or even amplify those patterns. AI may also rely on factors that seem neutral but are still connected to race, sex, age, or other protected characteristics. Both can potentially lead to discriminatory outcomes.

Whether AI is being used for recruiting, layoffs, or other employment decisions, HR's responsibility doesn’t disappear because technology is involved. Practical steps employers can take include:

• Understand how AI tools are being used. Know what employment decisions they influence, what factors and data they consider, and how feedback or past outcomes may affect future recommendations.
• Continually monitor outcomes. Regardless of a vendor’s overall assessment, look at what the tool is actually producing within your organization and by individual position. 
• Maintain meaningful human oversight. Establish who owns the employment decision and document how the tool is used in the decision-making process. 

AI may make employment decisions faster and more efficient. But automation doesn’t eliminate accountability for the decisions being made.