mlsanityData to 7 Oct 2026

Challenging the appearance of machine intelligence: Cognitive bias in LLMs and Best Practices for Adoption

arXiv 2304.01358 ↗First posted 3 Apr 2023cs.HCcited by 21

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Abstract (arXiv)

Assessments of algorithmic bias in large language models (LLMs) are generally catered to uncovering systemic discrimination based on protected characteristics such as sex and ethnicity. However, there are over 180 documented cognitive biases that pervade human reasoning and decision making that are routinely ignored when discussing the ethical complexities of AI. We demonstrate the presence of these cognitive biases in LLMs and discuss the implications of using biased reasoning under the guise of expertise. We call for stronger education, risk management, and continued research as widespread adoption of this technology increases. Finally, we close with a set of best practices for when and how to employ this technology as widespread adoption continues to grow.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Apr 2023.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 14; 2025: 1; 2026: 1.

21 papers.

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  1. 2023Benchmarking Cognitive Biases in Large Language Models as Evaluatorscited by 147
  2. 2023Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineeringcited by 125
  3. 2024Cognitive Bias in Decision-Making with LLMscited by 104
  4. 2024A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasonerscited by 73
  5. 2024Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challengescited by 48
  6. 2023Analyzing the Inherent Response Tendency of LLMs: Real-World Instructions-Driven Jailbreakcited by 22
  7. 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 21
  8. 2024Cognitive Biases in Large Language Models: A Survey and Mitigation Experimentscited by 20
  9. 2024Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Reviewcited by 17
  10. 2024Large Language Model Benchmarks in Medical Taskscited by 16
  11. 2024Comparing Inferential Strategies of Humans and Large Language Models in Deductive Reasoningcited by 13
  12. 2024Explaining Large Language Models Decisions Using Shapley Valuescited by 12
9 more
  1. 2024CBEval: A framework for evaluating and interpreting cognitive biases in LLMscited by 12
  2. 2023Cognitive bias in large language models: Cautious optimism meets anti-Panglossian meliorismcited by 4
  3. 2024OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Modelscited by 4
  4. 2024What is the Role of Large Language Models in the Evolution of Astronomy Research?cited by 4
  5. 2024Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Biascited by 4
  6. 2025Distinguishing AI-Generated and Human-Written Text Through Psycholinguistic Analysiscited by 3
  7. 2023Bridging the Gap: Exploring the Capabilities of Bridge-Architectures for Complex Visual Reasoning Taskscited by 2
  8. 2026DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmarkcited by 1
  9. 2024People will agree what I think: Investigating LLM's False Consensus Effectcited by 0