# Confirmation Bias in Generative AI Chatbots: Mechanisms, Risks, Mitigation Strategies, and Future Research Directions

arXiv: https://arxiv.org/abs/2504.09343 · HTML: https://mlsanity.com/paper/2504.09343/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 12 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023)
- [Bias and Debias in Recommender System: A Survey and Future Directions](https://mlsanity.com/paper/2010.03240/) (2020)
- [Large language models in healthcare and medical domain: A review](https://mlsanity.com/paper/2401.06775/) (2023)
- [When Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities](https://mlsanity.com/paper/2307.16376/) (2023)
- [Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap](https://mlsanity.com/paper/2401.10034/) (2024)
- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [Bias and Fairness in Chatbots: An Overview](https://mlsanity.com/paper/2309.08836/) (2023)
- [Modeling confirmation bias and polarization](https://mlsanity.com/paper/1607.00022/) (2016)

## Built on by (most cited first, 1 of 1)

- [Measuring Algorithmic Partisanship via Zero-Shot Classification and Its Implications on Political Discourse](https://mlsanity.com/paper/2510.01258/) (2025)

## Abstract (arXiv)

This article explores the phenomenon of confirmation bias in generative AI chatbots, a relatively underexamined aspect of AI-human interaction. Drawing on cognitive psychology and computational linguistics, it examines how confirmation bias, commonly understood as the tendency to seek information that aligns with existing beliefs, can be replicated and amplified by the design and functioning of large language models. The article analyzes the mechanisms by which confirmation bias may manifest in chatbot interactions, assesses the ethical and practical risks associated with such bias, and proposes a range of mitigation strategies. These include technical interventions, interface redesign, and policy measures aimed at promoting balanced AI-generated discourse. The article concludes by outlining future research directions, emphasizing the need for interdisciplinary collaboration and empirical evaluation to better understand and address confirmation bias in generative AI systems.