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

arXiv: https://arxiv.org/abs/2304.01358 · HTML: https://mlsanity.com/paper/2304.01358/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2023.

## Built on by (most cited first, 21 of 21)

- [Benchmarking Cognitive Biases in Large Language Models as Evaluators](https://mlsanity.com/paper/2309.17012/) (2023)
- [Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering](https://mlsanity.com/paper/2305.03403/) (2023)
- [Cognitive Bias in Decision-Making with LLMs](https://mlsanity.com/paper/2403.00811/) (2024)
- [A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners](https://mlsanity.com/paper/2406.11050/) (2024)
- [Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges](https://mlsanity.com/paper/2409.02387/) (2024)
- [Analyzing the Inherent Response Tendency of LLMs: Real-World Instructions-Driven Jailbreak](https://mlsanity.com/paper/2312.04127/) (2023)
- [Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models](https://mlsanity.com/paper/2407.13934/) (2024)
- [Cognitive Biases in Large Language Models: A Survey and Mitigation Experiments](https://mlsanity.com/paper/2412.00323/) (2024)
- [Exploring the Frontiers of LLMs in Psychological Applications: A Comprehensive Review](https://mlsanity.com/paper/2401.01519/) (2024)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Comparing Inferential Strategies of Humans and Large Language Models in Deductive Reasoning](https://mlsanity.com/paper/2402.14856/) (2024)
- [Explaining Large Language Models Decisions Using Shapley Values](https://mlsanity.com/paper/2404.01332/) (2024)
- [CBEval: A framework for evaluating and interpreting cognitive biases in LLMs](https://mlsanity.com/paper/2412.03605/) (2024)
- [Cognitive bias in large language models: Cautious optimism meets anti-Panglossian meliorism](https://mlsanity.com/paper/2311.10932/) (2023)
- [OMGEval: An Open Multilingual Generative Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2402.13524/) (2024)
- [What is the Role of Large Language Models in the Evolution of Astronomy Research?](https://mlsanity.com/paper/2409.20252/) (2024)
- [Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Bias](https://mlsanity.com/paper/2410.13868/) (2024)
- [Distinguishing AI-Generated and Human-Written Text Through Psycholinguistic Analysis](https://mlsanity.com/paper/2505.01800/) (2025)
- [Bridging the Gap: Exploring the Capabilities of Bridge-Architectures for Complex Visual Reasoning Tasks](https://mlsanity.com/paper/2307.16395/) (2023)
- [DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmark](https://mlsanity.com/paper/2608.30413/) (2026)
- [People will agree what I think: Investigating LLM's False Consensus Effect](https://mlsanity.com/paper/2407.12007/) (2024)

## 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.