# Heuristics and Biases in AI Decision-Making: Implications for Responsible AGI

arXiv: https://arxiv.org/abs/2410.02820 · HTML: https://mlsanity.com/paper/2410.02820/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Thinking Fast and Slow in Large Language Models](https://mlsanity.com/paper/2212.05206/) (2022)
- [Capturing Failures of Large Language Models via Human Cognitive Biases](https://mlsanity.com/paper/2202.12299/) (2022)
- [(Ir)rationality and Cognitive Biases in Large Language Models](https://mlsanity.com/paper/2402.09193/) (2024)
- [Cognitive Effects in Large Language Models](https://mlsanity.com/paper/2308.14337/) (2023)
- [Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems](https://mlsanity.com/paper/2203.09911/) (2022)
- [BIASeD: Bringing Irrationality into Automated System Design](https://mlsanity.com/paper/2210.01122/) (2022)

## Built on by (most cited first, 2 of 2)

- [The Fragility of Strategic Thinking in Large Language Models](https://mlsanity.com/paper/2510.10813/) (2025)
- [GMP: A Benchmark for Content Moderation under Co-occurring Violations and Dynamic Rules](https://mlsanity.com/paper/2603.01724/) (2026)

## Abstract (arXiv)

We investigate the presence of cognitive biases in three large language models (LLMs): GPT-4o, Gemma 2, and Llama 3.1. The study uses 1,500 experiments across nine established cognitive biases to evaluate the models' responses and consistency. GPT-4o demonstrated the strongest overall performance. Gemma 2 showed strengths in addressing the sunk cost fallacy and prospect theory, however its performance varied across different biases. Llama 3.1 consistently underperformed, relying on heuristics and exhibiting frequent inconsistencies and contradictions. The findings highlight the challenges of achieving robust and generalizable reasoning in LLMs, and underscore the need for further development to mitigate biases in artificial general intelligence (AGI). The study emphasizes the importance of integrating statistical reasoning and ethical considerations in future AI development.