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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2403.16101 |
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| _version_ | 1866915276547686400 |
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| author | Sasaki, Yuya Tokuno, Sohei Maeda, Haruka Nakajima, Kazuki Sakura, Osamu Fletcher, George Pechenizkiy, Mykola Karras, Panagiotis Shklovski, Irina |
| author_facet | Sasaki, Yuya Tokuno, Sohei Maeda, Haruka Nakajima, Kazuki Sakura, Osamu Fletcher, George Pechenizkiy, Mykola Karras, Panagiotis Shklovski, Irina |
| contents | Which fairness metrics are appropriately applicable in your contexts? There may be instances of discordance regarding the perception of fairness, even when the outcomes comply with established fairness metrics. Several questionnaire-based surveys have been conducted to evaluate fairness metrics with human perceptions of fairness. However, these surveys were limited in scope, including only a few hundred participants within a single country. In this study, we conduct an international survey to evaluate public perceptions of various fairness metrics in decision-making scenarios. We collected responses from 1,000 participants in each of China, France, Japan, and the United States, amassing a total of 4,000 participants, to analyze the preferences of fairness metrics. Our survey consists of three distinct scenarios paired with four fairness metrics. This investigation explores the relationship between personal attributes and the choice of fairness metrics, uncovering a significant influence of national context on these preferences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16101 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Public Perceptions of Fairness Metrics Across Borders Sasaki, Yuya Tokuno, Sohei Maeda, Haruka Nakajima, Kazuki Sakura, Osamu Fletcher, George Pechenizkiy, Mykola Karras, Panagiotis Shklovski, Irina Artificial Intelligence Which fairness metrics are appropriately applicable in your contexts? There may be instances of discordance regarding the perception of fairness, even when the outcomes comply with established fairness metrics. Several questionnaire-based surveys have been conducted to evaluate fairness metrics with human perceptions of fairness. However, these surveys were limited in scope, including only a few hundred participants within a single country. In this study, we conduct an international survey to evaluate public perceptions of various fairness metrics in decision-making scenarios. We collected responses from 1,000 participants in each of China, France, Japan, and the United States, amassing a total of 4,000 participants, to analyze the preferences of fairness metrics. Our survey consists of three distinct scenarios paired with four fairness metrics. This investigation explores the relationship between personal attributes and the choice of fairness metrics, uncovering a significant influence of national context on these preferences. |
| title | Public Perceptions of Fairness Metrics Across Borders |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2403.16101 |