# Fair Machine Guidance to Enhance Fair Decision Making in Biased People

arXiv: https://arxiv.org/abs/2404.05228 · HTML: https://mlsanity.com/paper/2404.05228/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2024.

## Built on by (most cited first, 7 of 7)

- [Aligning Generalisation Between Humans and Machines](https://mlsanity.com/paper/2411.15626/) (2024)
- [Safety Without Semantic Disruptions: Editing-free Safe Image Generation via Context-preserving Dual Latent Reconstruction](https://mlsanity.com/paper/2411.13982/) (2024)
- [More Isn't Always Better: Balancing Decision Accuracy and Conformity Pressures in Multi-AI Advice](https://mlsanity.com/paper/2603.22152/) (2026)
- [Towards Objective and Unbiased Decision Assessments with LLM-Enhanced Hierarchical Attention Networks](https://mlsanity.com/paper/2411.08504/) (2024)
- ["I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment](https://mlsanity.com/paper/2509.17956/) (2025)
- [Empowering Affected Individuals to Shape AI Fairness Assessments: Processes, Criteria, and Tools](https://mlsanity.com/paper/2602.06984/) (2026)
- [Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflicts](https://mlsanity.com/paper/2603.01457/) (2026)

## Abstract (arXiv)

Teaching unbiased decision-making is crucial for addressing biased decision-making in daily life. Although both raising awareness of personal biases and providing guidance on unbiased decision-making are essential, the latter topics remains under-researched. In this study, we developed and evaluated an AI system aimed at educating individuals on making unbiased decisions using fairness-aware machine learning. In a between-subjects experimental design, 99 participants who were prone to bias performed personal assessment tasks. They were divided into two groups: a) those who received AI guidance for fair decision-making before the task and b) those who received no such guidance but were informed of their biases. The results suggest that although several participants doubted the fairness of the AI system, fair machine guidance prompted them to reassess their views regarding fairness, reflect on their biases, and modify their decision-making criteria. Our findings provide insights into the design of AI systems for guiding fair decision-making in humans.