Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News
This addresses biases in collective decision-making for fake news detection, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of biases in human judgment affecting fake news detection, showing that demographic factors and presentation significantly influence errors, and adaptive aggregation algorithms improved accuracy while mitigating biases.
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.