How AI Assessment Tools Affect Job Candidates' Behavior
Most companies, when considering AI assessment tools, focus on the gains the tools bring in terms of efficiency and quality. But what they don't factor in is how AI assessment may change candidates' behavior during the assessment. Our new research, examining over 13,000 participants across 12 studies, reveals that this is a crucial blind spot. We looked at simulation of a variety of assessment situations in both the laboratory and the field, and we collaborated with a startup platform offering game-based hiring solutions called Equalture.
The results show that job candidates consistently emphasized analytical traits when they believed AI was evaluating them, while downplaying the very human qualities—empathy, creativity, intuition—that often distinguish outstanding employees from merely competent ones. This drove candidates to present a different and potentially more homogeneous version of themselves, in turn affecting who was likely to succeed in an AI-enabled hiring process, with implications for organizations using AI in hiring, promotions, or admission decisions.
Why This Matters for Your Organization
The implications of our findings extend beyond individual hiring decisions. When candidates systematically misrepresent themselves, organizations face several critical challenges:
Talent pool distortion: While AI is sometimes blamed for making biased hiring decisions (for example, discriminating against women in the selection process), our research suggests that knowing that one is assessed by AI also biases candidates, making them believe that they should prioritize their analytical capabilities. As a result, companies may be screening out exactly the candidates they need simply by using AI: that innovative thinker or emotionally intelligent leader you're looking for might present themselves as a rule-following analyst because they believe that is what the AI wants to see.
Validity compromise: Assessment tools are only as good as the data they collect. When candidates strategically alter their responses, the fundamental validity of the assessment process might be undermined. Organizations may no longer measure authentic capabilities—instead, they may measure what candidates think AI will value the most.
Unintended homogenization: If most candidates believe AI favors analytical traits, the talent pipeline may become increasingly uniform, potentially undermining diversity initiatives and limiting the range of perspectives in organizations. Companies like IBM and Hilton, which integrate AI into both hiring and internal promotion systems, must now contend with whether such tools nudge employees toward formulaic self-presentation.
New transparency regulations like the EU's AI Act, which require organizations to disclose AI use in high-stakes decisions, make these outcomes all the more likely. When candidates are aware that an AI is assessing them, they are more likely to change their behavior.
What Leaders Can Do
Based on our findings, organizations can take several concrete steps to address the AI assessment effect:
Radical transparency: Do not just disclose AI assessment—be explicit about what it actually evaluates. Clearly communicate that your AI can and does value diverse traits, including creativity, emotional intelligence, and intuitive problem-solving. This might include providing examples of successful candidates who demonstrated strong intuitive or creative capabilities. Currently, few companies seem to be transparent about what exactly it is that AI assesses—at least this information is not easily accessible when clicking through career page information on the websites of many major companies. That said, applicants discuss and share their intuitions on blogs and videos, which may be counterproductive because it may or may not align with actual practices. We advise companies not to leave their candidates to speculate.
Regular behavioral audits: Implement systematic reviews of your AI assessment outcomes. For instance, New York City has enacted Local Law 144, requiring employers to conduct annual bias audits of AI-based hiring. In response, one of the market leaders in AI-based hiring, HireVue reports their recent audits for race or gender bias across jobs and use cases. In addition to examining biases regarding demographics, we suggest using these audits to look for patterns indicating behavioral adaptation: Are candidates' responses becoming more homogeneous over time? Are you seeing a shift toward analytical presentations at the expense of other valuable traits?
Hybrid assessment: Some organizations combine human and AI assessments. For example, Salesforce notes that besides technology, a human will review applications. Nvidia and Philip Morris International guarantee ultimate assessment and decision-making through a human. One of our studies shows that while this hybrid human assessment does reduce candidates' tendency to highlight analytical capabilities, it does not eliminate it. To close the gap, you need to train your human hirers to compensate for the AI effect.
The Path Forward
As AI becomes increasingly embedded in organizational decision-making, we must recognize that these tools do not just change processes—they change people. The efficiency gains from AI assessment may come at the cost of authentic candidate presentation and, ultimately, the human diversity that makes organizations innovative and resilient. The irony is striking: In our quest to remove human bias from hiring, we may have created a system where AI introduces a new form of bias. The solution is not to abandon AI, but to design assessment systems that account for and counteract these behavioral shifts. Only by keeping humans—not just metrics—at the heart of our assessment strategies can we build hiring systems that truly identify and nurture the diverse talent our organizations need.
Hashtags
Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles
Bloomberg
11 minutes ago
- Bloomberg
Alibaba Cloud Visionary Expects Big Shakeup After OpenAI Hype
OpenAI 's ChatGPT started a revolution in artificial intelligence development and investment. Yet nine-tenths of the technology and services that've sprung up since could be gone in under a decade, according to the founder of Alibaba Group Holding Ltd. 's cloud and AI unit. The problem is the US startup, celebrated for ushering AI into the mainstream, created 'bias' or a skewed understanding of what AI can do, Wang Jian told Bloomberg Television. It fired the popular imagination about chatbots, but the plethora of applications for AI goes far beyond that. Developers need to cut through the noise and think creatively about applications to propel the next stage of AI development, said Wang, who built Alibaba's now second-largest business from scratch in 2009.
Forbes
2 hours ago
- Forbes
OpenAI: ChatGPT Wants Legal Rights. You Need The Right To Be Forgotten.
As systems like ChatGPT move toward achieving legal privilege, the boundaries between identity, ... More memory, and control are being redefined, often without consent. When OpenAI CEO Sam Altman recently stated that conversations with ChatGPT should one day enjoy legal privilege, similar to those between a patient and a doctor or a client and a lawyer, he wasn't just referring to privacy. He was pointing toward a redefinition of the relationship between people and machines. Legal privilege protects the confidentiality of certain relationships. What's said between a patient and physician, or a client and attorney, is shielded from subpoenas, court disclosures, and adversarial scrutiny. Extending that same protection to AI interactions means treating the machine not as a tool, but as a participant in a privileged exchange. This is more than a policy suggestion. It's a legal and philosophical shift with consequences no one has fully reckoned with. It also comes at a time when the legal system is already being tested. In The New York Times' lawsuit against OpenAI, the paper has asked courts to compel the company to preserve all user prompts, including those the company says are deleted after 30 days. That request is under appeal. Meanwhile, Altman's suggestion that AI chats deserve legal shielding raises the question: if they're protected like therapy sessions, what does that make the system listening on the other side? People are already treating AI like a confidant. According to Common Sense Media, three in four teens have used an AI chatbot, and over half say they trust the advice they receive at least somewhat. Many describe a growing reliance on these systems to process everything from school to relationships. Altman himself has called this emotional over-reliance 'really bad and dangerous.' But it's not just teens. AI is being integrated into therapeutic apps, career coaching tools, HR systems, and even spiritual guidance platforms. In some healthcare environments, AI is being used to draft communications and interpret lab data before a doctor even sees it. These systems are present in decision-making loops, and their presence is being normalized. This is how it begins. First, protect the conversation. Then, protect the system. What starts as a conversation about privacy quickly evolves into a framework centered on rights, autonomy, and standing. We've seen this play out before. In U.S. law, corporations were gradually granted legal personhood, not because they were considered people, but because they acted as consistent legal entities that required protection and responsibility under the law. Over time, personhood became a useful legal fiction. Something similar may now be unfolding with AI—not because it is sentient, but because it interacts with humans in ways that mimic protected relationships. The law adapts to behavior, not just biology. The Legal System Isn't Ready For What ChatGPT Is Proposing There is no global consensus on how to regulate AI memory, consent, or interaction logs. The EU's AI Act introduces transparency mandates, but memory rights are still undefined. In the U.S., state-level data laws conflict, and no federal policy yet addresses what it means to interact with a memory‑enabled AI. (See my recent Forbes piece on why AI regulation is effectively dead—and what businesses need to do instead.) The physical location of a server is not just a technical detail. It's a legal trigger. A conversation stored on a server in California is subject to U.S. law. If it's routed through Frankfurt, it becomes subject to GDPR. When AI systems retain memory, context, and inferred consent, the server location effectively defines sovereignty over the interaction. That has implications for litigation, subpoenas, discovery, and privacy. 'I almost wish they'd go ahead and grant these AI systems legal personhood, as if they were therapists or clergy,' says technology attorney John Kheit. 'Because if they are, then all this passive data collection starts to look a lot like an illegal wiretap, which would thereby give humans privacy rights/protections when interacting with AI. It would also, then, require AI providers to disclose 'other parties to the conversation', i.e., that the provider is a mining party reading the data, and if advertisers are getting at the private conversations.' Infrastructure choices are now geopolitical. They determine how AI systems behave under pressure and what recourse a user has when something goes wrong. And yet, underneath all of this is a deeper motive: monetization. But they won't be the only ones asking questions. Every conversation becomes a four-party exchange: the user, the model, the platform's internal optimization engine, and the advertiser paying for access. It's entirely plausible for a prompt about the Pittsburgh Steelers to return a response that subtly inserts 'Buy Coke' mid-paragraph. Not because it's relevant—but because it's profitable. Recent research shows users are significantly worse at detecting unlabeled advertising when it's embedded inside AI-generated content. Worse, these ads are initially rated as more trustworthy until users discover they are, in fact, ads. At that point, they're also rated as more manipulative. 'In experiential marketing, trust is everything,' says Jeff Boedges, Founder of Soho Experiential. 'You can't fake a relationship, and you can't exploit it without consequence. If AI systems are going to remember us, recommend things to us, or even influence us, we'd better know exactly what they remember and why. Otherwise, it's not personalization. It's manipulation.' Now consider what happens when advertisers gain access to psychographic modeling: 'Which users are most emotionally vulnerable to this type of message?' becomes a viable, queryable prompt. And AI systems don't need to hand over spreadsheets to be valuable. With retrieval-augmented generation (RAG) and reinforcement learning from human feedback (RLHF), the model can shape language in real time based on prior sentiment, clickstream data, and fine-tuned advertiser objectives. This isn't hypothetical—it's how modern adtech already works. At that point, the chatbot isn't a chatbot. It's a simulation environment for influence. It is trained to build trust, then designed to monetize it. Your behavioral patterns become the product. Your emotional response becomes the target for optimization. The business model is clear: black-boxed behavioral insight at scale, delivered through helpful design, hidden from oversight, and nearly impossible to detect. We are entering a phase where machines will be granted protections without personhood, and influence without responsibility. If a user confesses to a crime during a legally privileged AI session, is the platform compelled to report it or remain silent? And who makes that decision? These are not edge cases. They are coming quickly. And they are coming at scale. Why ChatGPT Must Remain A Model—and Why Humans Must Regain Consent As generative AI systems evolve into persistent, adaptive participants in daily life, it becomes more important than ever to reassert a boundary: models must remain models. They cannot assume the legal, ethical, or sovereign status of a person quietly. And the humans generating the data that train these systems must retain explicit rights over their contributions. What we need is a standardized, enforceable system of data contracting, one that allows individuals to knowingly, transparently, and voluntarily contribute data for a limited, mutually agreed-upon window of use. This contract must be clear on scope, duration, value exchange, and termination. And it must treat data ownership as immutable, even during active use. That means: When a contract ends, or if a company violates its terms, the individual's data must, by law, be erased from the model, its training set, and any derivative products. 'Right to be forgotten' must mean what it says. But to be credible, this system must work both ways: This isn't just about ethics. It's about enforceable, mutual accountability. The user experience must be seamless and scalable. The legal backend must be secure. And the result should be a new economic compact—where humans know when they're participating in AI development, and models are kept in their place. ChatGPT Is Changing the Risk Surface. Here's How to Respond. The shift toward AI systems as quasi-participants—not just tools—will reshape legal exposure, data governance, product liability, and customer trust. Whether you're building AI, integrating it into your workflows, or using it to interface with customers, here are five things you should be doing immediately: ChatGPT May Get Privilege. You Should Get the Right to Be Forgotten. This moment isn't just about what AI can do. It's about what your business is letting it do, what it remembers, and who gets access to that memory. Ignore that, and you're not just risking privacy violations, you're risking long-term brand trust and regulatory blowback. At the very least, we need a legal framework that defines how AI memory is governed. Not as a priest, not as a doctor, and not as a partner, but perhaps as a witness. Something that stores information and can be examined when context demands it, with clear boundaries on access, deletion, and use. The public conversation remains focused on privacy. But the fundamental shift is about control. And unless the legal and regulatory frameworks evolve rapidly, the terms of engagement will be set, not by policy or users, but by whoever owns the box. Which is why, in the age of AI, the right to be forgotten may become the most valuable human right we have. Not just because your data could be used against you—but because your identity itself can now be captured, modeled, and monetized in ways that persist beyond your control. Your patterns, preferences, emotional triggers, and psychological fingerprints don't disappear when the session ends. They live on inside a system that never forgets, never sleeps, and never stops optimizing. Without the ability to revoke access to your data, you don't just lose privacy. You lose leverage. You lose the ability to opt out of prediction. You lose control over how you're remembered, represented, and replicated. The right to be forgotten isn't about hiding. It's about sovereignty. And in a world where AI systems like ChatGPT will increasingly shape our choices, our identities, and our outcomes, the ability to walk away may be the last form of freedom that still belongs to you.
Yahoo
3 hours ago
- Yahoo
Broadcom is no longer the 'poor man's Nvidia' in the AI race
Artificial intelligence (AI) continues to be a key theme of Big Tech earnings, as Alphabet (GOOG, GOOGL) kicked off "Magnificent Seven" earnings with very high additional AI capital expenditure (CapEx), a positive sign for AI chipmakers. Nancy Tengler, CEO and chief investment officer of Laffer Tengler Investments, and Stacy Rasgon, managing director and senior analyst at Bernstein, share their thoughts on two major AI chip players: Nvidia (NVDA) and Broadcom (AVGO). To watch more expert insights and analysis on the latest market action, check out more Market Catalysts here. I think you guys are on the same page when it comes to Nvidia. You've got a buy equivalent rating on it, Stacey. Nancy likes that one. Let's talk about Broadcom for a minute because Nancy, this is one that you've liked for a while. Um, do you still like it? What are you going to be looking for from Broadcom, Nancy, going forward? Yeah, we do, Julie. I mean, it's our largest holding across all of our large cap equity strategies, member of our 12 best, our five for 25. I think, uh, and it's outperformed Nvidia pretty handily over the last year, almost double the returns for Nvidia on a trailing one year. We've always called it the poor man's Nvidia. I think we're going to have to come up with a new name. But one of the things that we're going to be paying attention to is, of course, um, the AI revenues. We we've we've seen those compound at 60 plus percent. They've announced new partnerships. We want to hear more about that. Um, it seems that the rest of the business, the rest of the chip business may have bottomed. We'd like to hear some some information and and confirmation about that. And then I just think, you know, it's just going to be about the future guidance. And Hock Tan has demonstrated he can acquire companies, make them accretive quickly. We bought the stock when, uh, it sold off on the computer associates that used to be the name of the company they acquired. Wall Street didn't like it. They turned it around, made it a very positive acquisition. So we we'll be listening for that, too. Are there any acquisitions they're they're planning to make? And I certainly hope one of them is not Intel. Yeah, that would be something. Uh, Stacey, um, along with Nvidia, is Broadcom sort of, are those the sort of must-owns in the chip space? Yeah, I frankly, in in chips, it hasn't been great outside of AI, right? I mean, AI's been super strong. The analog, more diversified guys, like the people who were playing those on cyclical recovery, some of those prints so far this earning season have not not been so great. There's worries about pull forward and everything else. Again, you got companies like Intel which which frankly are a basket case. I mean, if it wasn't for AI, this space would not be doing very well. So I I do like the AI names. We cover Nvidia and Broadcom. I like them both. Um, Broadcom is is just more expensive than it used to be. That's the only only, you know, it was Right. I guess hence Nancy's comment that they're going to have to rename it from the poor man's video. Yeah, and look, you know, you got to remember Broadcom like not all that long ago was like 16 times earnings, like now it's like the multiple's like like doubled, right? Um, they are showing a lot of AI upside. A lot of that comes next year, but they they're clearly, I mean even the last earnings call a couple of months ago, they're clearly calling for upside in their AI revenues next year on more inference demand. They're a massive player on AI networking, right? So there's there there's a a big play there. And and and Nancy, I think, is right, they have the core semi business which admittedly has been lousy. They're not the only ones. Everybody in though in those kinds of markets has has been lousy. It it doesn't look like getting any worse at least. I we can we can argue about when it's going to start getting better. I don't know yet, but at least it isn't getting worse. Um, you know, if you're looking in in into the near term, I mean you could argue, again, we we like both stocks. Nvidia is cheaper. And you know, you know, they just they just got their China licenses, um, reinstated, so there's probably more upside to their AI numbers this year for Nvidia versus Broadcom. I think the Broadcom AI upside comes next year and Broadcom's a little more expensive. Um, and then there's a whole ASIC versus, you know, GPU debate. But I I think you can own them both. Like I I I like them both. And again, AI is the only thing in semis right now that fundamentally is really working.
