# Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News

arXiv: https://arxiv.org/abs/2403.08829 · HTML: https://mlsanity.com/paper/2403.08829/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 4 of 4)

- [Efficient Contextual Bandits in Non-stationary Worlds](https://mlsanity.com/paper/1708.01799/) (2017)
- [Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice](https://mlsanity.com/paper/1810.07778/) (2018)
- [Dealing with Expert Bias in Collective Decision-Making](https://mlsanity.com/paper/2106.13539/) (2021)
- [Expertise Trees Resolve Knowledge Limitations in Collective Decision-Making](https://mlsanity.com/paper/2305.01063/) (2023)

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

- [Quantifying Cognitive Bias Induction in LLM-Generated Content](https://mlsanity.com/paper/2507.03194/) (2025)
- [Wisdom from Diversity: Bias Mitigation Through Hybrid Human-LLM Crowds](https://mlsanity.com/paper/2505.12349/) (2025)

## Abstract (arXiv)

Individual and social biases undermine the effectiveness of human advisers by inducing judgment errors which can disadvantage protected groups. In this paper, we study the influence these biases can have in the pervasive problem of fake news by evaluating human participants' capacity to identify false headlines. By focusing on headlines involving sensitive characteristics, we gather a comprehensive dataset to explore how human responses are shaped by their biases. Our analysis reveals recurring individual biases and their permeation into collective decisions. We show that demographic factors, headline categories, and the manner in which information is presented significantly influence errors in human judgment. We then use our collected data as a benchmark problem on which we evaluate the efficacy of adaptive aggregation algorithms. In addition to their improved accuracy, our results highlight the interactions between the emergence of collective intelligence and the mitigation of participant biases.