mlsanityData to 14 Oct 2026

Are AI Decision Models Biased? A Case Study of Jev

arXiv 2610.19619 ↗First posted 14 Oct 2026cs.AIcited by 0

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Abstract (arXiv)

Do AI models designed for fast, structured decisions exhibit the cognitive and social biases observed in human judgment? We evaluate Jev, a model described as a System One decision-maker, using 41,500 responses across cognitive reflection tasks, five classical cognitive-bias paradigms, and a pricing experiment testing ethnic and gender bias. At the level of selected answers, the results are largely encouraging. We confirm previous findings that Jev performs extremely well on cognitive reflection problems. We also observe that it generally avoids the characteristic choice patterns associated with several well-documented human biases, and exhibits only small differences among demographic groups in the tested pricing scenario. However, this apparent robustness conceals important vulnerabilities. Its judgments are sensitive to irrelevant numerical information and the ordering of response options. Moreover, several cognitive-bias manipulations substantially affect returned probabilities even when selected answers remain unchanged. Most strikingly, Jev answers a well-known probability problem correctly in every response but violates the same underlying probability rule in every response to an adapted version. We investigate this discrepancy using 15,000 additional responses across 30 conjunction tasks. Jev violates the rule in 54% of responses, with accuracy varying dramatically across formulations and answer orders. These findings suggest that evaluating decision models solely on familiar benchmarks and selected answers may substantially overstate their reliability in practical applications. They also identify vulnerabilities for developers to address and practical considerations for users integrating such models into automated decision-making systems.

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References · 18

  1. 2023Large Language Models are not Fair Evaluatorscited by 1,085
  2. 2022Out of One, Many: Using Language Models to Simulate Human Samplescited by 752
  3. 2021BBQ: A Hand-Built Bias Benchmark for Question Answeringcited by 701
  4. 2022Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studiescited by 515
  5. 2023Large Language Models Are Not Robust Multiple Choice Selectorscited by 486
  6. 2022Using cognitive psychology to understand GPT-3cited by 398
  7. 2022Thinking Fast and Slow in Large Language Modelscited by 137
  8. 2023Evaluating and Mitigating Discrimination in Language Model Decisionscited by 124
  9. 2024Cognitive Bias in Decision-Making with LLMscited by 106
  10. 2024CogBench: a large language model walks into a psychology labcited by 63
  11. 2023Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Biascited by 53
  12. 2023Do Large Language Models Show Decision Heuristics Similar to Humans? A Case Study Using GPT-3.5cited by 42
6 more
  1. 2024A Comprehensive Evaluation of Cognitive Biases in LLMscited by 34
  2. 2023Social Bias Probing: Fairness Benchmarking for Language Modelscited by 32
  3. 2026Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failurescited by 8
  4. 2026More Choices, Fewer Decisions: Ordinal-Scale Bias in JEV-like Direct-Decision Modelscited by 2
  5. 2026Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnessescited by 2
  6. 2026Code Owns the Simulation, Jev Owns the Evaluationcited by 1

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