Mitigating Biases in Collective Decision-Making: Enhancing Performance
  in the Face of Fake News
Axel Abels Elias Fernandez Domingos Ann Nowé Tom Lenaerts
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.