Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916159499010048 |
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| author | Abels, Axel Domingos, Elias Fernandez Nowé, Ann Lenaerts, Tom |
| author_facet | Abels, Axel Domingos, Elias Fernandez Nowé, Ann Lenaerts, Tom |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_08829 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News Abels, Axel Domingos, Elias Fernandez Nowé, Ann Lenaerts, Tom Human-Computer Interaction Machine Learning Social and Information Networks 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. |
| title | Mitigating Biases in Collective Decision-Making: Enhancing Performance in the Face of Fake News |
| topic | Human-Computer Interaction Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2403.08829 |