AI systems judge people with systematic biases stronger than humans, study reveals

AI trust is structured, rigid, and may be more biased than humans

AI systems do not think like humans -- they may even be more biased * AI mimics trust while relying on rigid, structured evaluation patterns * Machines separate human traits instead of forming holistic impressions * Competence and integrity dominate decisions across both humans and AI Modern AI systems do not simply process information; they make systematic judgments about people in ways that resemble human trust but with important differences. A new study from Hebrew University, published in Proceedings of the Royal Society, analyzed over 43,000 simulated decisions alongside around a thousand human participants across five scenarios. These scenarios included deciding how much money to lend a small business owner, whether to trust a babysitter, how to rate a boss, and how much to donate to a nonprofit founder. How AI breaks down human judgment into separate columns The findings reveal that AI tools form something that looks like trust, but their judgment works very differently from ours. Both humans and AI favored people who seemed competent, honest, and well-intentioned, meaning machines captured something real about human trust. "That's the good news," said Prof. Yaniv Dover. "AI is not making random decisions. It captures something real about how humans evaluate one another." However, humans tend to form a general impression, blending multiple traits into a single, intuitive, and holistic judgment. AI does something very different: it breaks people down into components, scoring competence, integrity, and kindness, almost like separate columns in a spreadsheet. "People in our study are messy and holistic in how they judge others," explained Valeria Lerman. "AI is cleaner, more systematic, and that can lead to very different outcomes." These differences appeared even when every other detail about the person was identical. "Humans have biases, of course," said Prof. Dover. "But what surprised us is that AI's biases can be more systematic, more predictable, and sometimes stronger." In financial scenarios such as deciding how much money to lend or donate, AI systems showed consistent differences based solely on demographic traits. Older individuals were frequently given more favorable outcomes, religion had strong effects, especially in monetary scenarios, and gender also influenced decisions in certain models. Another key insight is that there is no single "AI opinion." Different models often made different judgments about the same person. This means that the choice of an AI system could quietly shape real-world outcomes. "Which model you use really matters," Lerman noted. Large language models are already being used to screen job candidates, assess creditworthiness, recommend medical actions, and guide organizational decisions. The study suggests that while AI can mimic the structure of human judgment, it does so in a more rigid, less nuanced way, with biases that may be harder to detect. "These systems are powerful," said Dover. "They can model aspects of human reasoning in a consistent way. But they are not human, and we should not assume they see people the way we do." As AI tools and AI agents move from assistants to decision makers, understanding how it "thinks" becomes critical for organizations deploying it at scale. The researchers emphasize that their findings are not a warning against AI, but rather a call for awareness. That said, the question is no longer whether we trust machines; it is whether we understand how they trust us. Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews, and opinion in your feeds.

AI systems show systematic biases in decision-making, study finds

AI systems exhibit predictable and systematic biases when judging people, according to a recent study from Hebrew University published in the Proceedings of the Royal Society. The research analyzed over 43,000 simulated decisions alongside approximately 1,000 human participants across five scenarios, revealing significant differences between human and AI evaluations. The scenarios included financial decisions like lending money to a small business owner, as well as social judgments such as assessing a babysitter or deciding how much to donate to a nonprofit founder. The findings indicate that while both humans and AI favored individuals perceived as competent, honest, and well-intentioned, machines operate using rigid evaluation criteria rather than forming holistic impressions. Prof. Yaniv Dover remarked, "AI is not making random decisions. It captures something real about how humans evaluate one another." However, the study noted that humans tend to create general impressions based on multiple traits, while AI assesses traits such as competence and integrity in a more segmented manner. Valeria Lerman further explained, "People in our study are messy and holistic in how they judge others. AI is cleaner, more systematic, and that can lead to very different outcomes." Differences in evaluation outcomes were apparent even when identical details about subjects were used. The researchers found that AI's biases could be more systematic and sometimes stronger than those of humans. In financial scenarios, AI systems displayed consistent biases favoring older individuals and were influenced by aspects like religion and gender. "Humans have biases, of course," said Dover. "But what surprised us is that AI's biases can be more systematic, more predictable, and sometimes stronger." The study revealed that varied AI models often produce different judgments about the same individual. This variability underscores the importance of AI model selection in determining real-world outcomes. "Which model you use really matters," Lerman noted. Currently, large language models are employed for job candidate screening, creditworthiness assessment, and other decision-making roles. While AI mimics human judgment processes, the researchers found it less nuanced and more rigid, often with biases that are harder to identify. "These systems are powerful," Dover emphasized. "They can model aspects of human reasoning in a consistent way. But they are not human, and we should not assume they see people the way we do." The researchers advocate for a deeper understanding of AI's evaluation processes as these tools evolve from assistants to decision-makers. They clarify that the goal is not to discourage the use of AI but to raise awareness regarding how these systems trust individuals. "The question is no longer whether we trust machines; it is whether we understand how they trust us," the study concludes.