Artificial intelligence (AI) is often seen as a neutral, objective force, capable of making quick, data-driven decisions without human error. But that perception doesn’t match reality. In truth, AI models are not conscious thinkers. They’re Large Language Models (LLMs) that generate words based on patterns in data. There’s no critical thinking, awareness, or ability to understand human nuance behind their responses. And when AI is used in place of real people, it can introduce serious problems, from bias to hallucinated “facts.”
One of the most pressing concerns is bias. Research has repeatedly shown that AI models reflect the data they were trained on, including the flaws. If the data is incomplete, one-sided, or culturally narrow, the model’s outputs will mirror those same blind spots. In some cases, this leads to gaps or inconsistencies in the information. In others, it can actively reinforce discrimination, particularly against marginalized communities.
In a recent TechCrunch interview, Giada Pistilli, Principal Ethicist and co-author of an AI study, explained, “Throughout our experiments, we found significant discrepancies in how models from different regions handle sensitive topics… Our research shows significant variation in the values conveyed by model responses, depending on culture and language.” For example, AI models trained in different regions often gave contradictory answers on topics like gender identity, health care, or politics, revealing how training data and developer assumptions can shape what an AI says.
One MIT Technology Review article warned that a health care chatbot might refuse to provide information on contraception, or a customer service bot might veer into offensive or irrelevant responses. Meanwhile, researchers at Carnegie Mellon even created a chart showing the political leanings of various AI tools—visual proof that different platforms reflect different built-in values.
These risks become even more serious when AI is used to support HR tasks like screening résumés, administering benefits, or offering mental health support. A Pennsylvania State University study found that AI models often discriminated against people with disabilities, particularly when unexplained gaps appeared in education or work history. Without accounting for the context behind disability-related language, these systems risked filtering out qualified candidates unfairly.
AI tools designed to flag hate speech have also been found to mislabel posts written in African American Vernacular English (AAVE) or other dialects, revealing that even the definition of “offensive language” is shaped by cultural context and data limitations. Bias can enter the equation at multiple points: when data is collected, labeled, coded into algorithms, trained, or deployed in real-world settings.
That’s why it’s so important for HR leaders to stay informed. Whether you’re hiring, supporting employees, or choosing a mental health partner, understanding the risks of AI bias is essential for creating a fair and inclusive workplace. At the end of this article, you’ll find a helpful list of the most common types of AI bias and how they show up in real-world applications.
Even beyond bias, AI has another major flaw: it can make things up. When a model lacks proper grounding in real-world facts or context, it may generate outputs that sound confident but are completely inaccurate. This is known as a “hallucination,” and in the world of HR and mental health, it can have serious consequences.
For example, if a mental health chatbot is trained only on data from adults with depression, it may miss or misinterpret signs of anxiety, trauma, or youth-specific issues. That can lead to poor recommendations, delayed care, or even harm. In more extreme cases, an AI tool might suggest treatments that don’t exist or give advice that’s completely unverified. And because these systems tend to speak with confidence, users may not realize when something is wrong.
When it comes to hiring, employee conflict resolution, or mental health benefits, hallucinated content can result in miscommunication, legal risks, or damage to trust. And with AI still in its early stages, the chances of error are high, especially when used without oversight or human judgment.
As AI becomes more embedded in workplace decision-making, it’s critical to remember that algorithms can’t fully understand human needs, especially when it comes to something as personal and complex as mental health. Relying on AI alone to guide benefits, hiring, or workplace support opens the door to serious missteps that harm both people and business. When biased data or hallucinated results influence benefit choices, employees may be left without the support they actually need, eroding trust, increasing burnout, and ultimately driving talent away. This is especially dangerous in the realm of mental health, where offering the wrong resources, or none at all, can have lasting consequences. That’s why it’s essential to choose solutions grounded in real human care. Tava Health connects employees with licensed therapists, not bots, ensuring care that’s personalized, effective, and rooted in empathy.
1. Algorithmic Bias: Occurs when flaws in an AI’s algorithm cause unfair or incorrect decisions.
Example: An AI recruiting tool starts favoring candidates who played baseball in college over those who played basketball, due to flawed patterns in past hiring data, leading to unfair exclusion of qualified applicants..
2. Sampling Bias: Happens when the data used to train AI isn’t random or representative of the real world.
Example: A survey for a wellness program is based only on the most engaged employees, leading the AI to wrongly conclude that everyone prefers yoga sessions, missing the needs of quieter or remote team members.
3. Selection Bias: Arises when the training data doesn’t reflect the population the AI will interact with.
Example: A promotion model trained primarily on data from corporate office employees performs poorly when used to evaluate field workers, resulting in overlooked talent in frontline roles.
4. Measurement Bias: Inaccuracies in how data is collected or defined.
Example: A performance evaluation tool trained only on data from top performers doesn't recognize the potential in newer or quieter employees, reinforcing an "only the loudest succeed" culture.
5. Predictive Bias: When AI consistently overestimates or underestimates future outcomes for certain groups.
Example: An AI used to predict high-potential candidates underestimates graduates from community colleges, based on outdated assumptions, even though many are top performers.
6. Cognitive Bias: Biases rooted in the humans who design, train, or label AI.
Example: A developer assumes people who use atypical speech patterns are untrustworthy and unfairly flags neurodivergent users in their algorithm that screens résumés, limiting the diversity of thought.
7. Exclusion Bias: Important factors are left out of the training data, skewing results.
Example: A retention model fails to include feedback from exit interviews or mental health utilization rates, overlooking key factors that contribute to burnout and turnover.
8. Out-Group Homogeneity Bias: AI generalizes individuals from underrepresented groups, assuming they’re all similar.
Example: A facial recognition login tool used for employee access misidentifies team members from underrepresented racial groups more frequently, leading to daily microaggressions and frustration.
9. Prejudice Bias: Cultural or societal stereotypes influence the AI’s output.
Example: A resume-screening AI trained on historical hiring patterns assumes that women are better suited for administrative roles, skewing interview invitations.
10. Stereotyping Bias: When AI reinforces common societal stereotypes.
Example: A chatbot that assists with benefit inquiries uses gendered language like "he" when discussing executives and "she" when referencing support staff, reinforcing workplace stereotypes.
11. Confirmation Bias: AI favors patterns that match past data, ignoring new trends.
Example: A recruitment tool favors male candidates because most past hires were men.
12. Recall Bias: Inconsistent labeling due to subjective human judgment.
Example: Employee reviews used to train a performance evaluation system are inconsistently labeled by managers—some rate assertiveness as "leadership," others as "aggressive," confusing the AI’s interpretation of desirable traits.
13. Reporting Bias: The training data reflects unusual or extreme examples, not typical ones.
Example: A workplace satisfaction model is trained only on Glassdoor reviews, which tend to be extremely positive or negative, so it misinterprets nuanced employee feedback collected internally.
14. Historical Bias: AI reflects and perpetuates past societal inequalities.
Example: A compensation model based on past salary data replicates historic gender pay gaps, recommending lower offers for female candidates despite equal qualifications.
15. Automation Bias: Over-relying on automated systems, even when they make more errors than humans.
Example: HR staff overly rely on an AI-powered hiring platform even when it misses great candidates flagged by recruiters, because “the system must know best.”
16. Coverage Bias: Data doesn’t fully represent all possible groups or scenarios.
Example: A model analyzing employee engagement is trained only on data from full-time staff, overlooking the needs and feedback of part-time, contract, or remote workers.
17. Non-Response Bias (Participation Bias): Certain groups are less likely to be represented because they don’t participate in data collection.
Example: A pulse survey about mental health support receives few responses from warehouse employees due to language barriers, so the AI assumes there’s no need for expanded benefits in that group.
18. Group Attribution Bias: Assuming what’s true for an individual applies to everyone in their group.
Example: A performance tool assumes all graduates from a prestigious university are high performers because a few alumni were standout employees, neglecting individual differences.
19. In-Group Bias: Favoring people who are similar to you.
Example: Two developers favor applicants from their alma mater, influencing the training data to prefer that school.
20. Implicit Bias: Unconscious beliefs influence how data is labeled or how models are built.
Example: A body language analysis tool flags candidates who avoid eye contact as less confident, penalizing neurodivergent individuals or those from cultures with different norms.
21. Experimenter’s Bias: Model builders adjust the process until the AI confirms their beliefs.
Example: An HR tech team tweaks a promotion-readiness model repeatedly until it confirms their gut feeling that long-tenured employees are the best future leaders, disregarding data showing new hires bringing innovation.
