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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.04511 |
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| _version_ | 1866917085235380224 |
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| author | Ayoobi, Hamed Potyka, Nico Rapberger, Anna Toni, Francesca |
| author_facet | Ayoobi, Hamed Potyka, Nico Rapberger, Anna Toni, Francesca |
| contents | As the use of AI in society grows, addressing emerging biases is essential to prevent systematic discrimination. Several bias detection methods have been proposed, but, with few exceptions, these tend to ignore transparency. Instead, interpretability and explainability are core requirements for algorithmic fairness, even more so than for other algorithmic solutions, given the human-oriented nature of fairness. We present ABIDE (Argumentative BIas detection by DEbate), a novel framework that structures bias detection transparently as debate, guided by an underlying argument graph as understood in (formal and computational) argumentation. The arguments are about the success chances of groups in local neighbourhoods and the significance of these neighbourhoods. We evaluate ABIDE experimentally and demonstrate its strengths in performance against an argumentative baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_04511 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Argumentative Debates for Transparent Bias Detection [Technical Report] Ayoobi, Hamed Potyka, Nico Rapberger, Anna Toni, Francesca Artificial Intelligence Machine Learning As the use of AI in society grows, addressing emerging biases is essential to prevent systematic discrimination. Several bias detection methods have been proposed, but, with few exceptions, these tend to ignore transparency. Instead, interpretability and explainability are core requirements for algorithmic fairness, even more so than for other algorithmic solutions, given the human-oriented nature of fairness. We present ABIDE (Argumentative BIas detection by DEbate), a novel framework that structures bias detection transparently as debate, guided by an underlying argument graph as understood in (formal and computational) argumentation. The arguments are about the success chances of groups in local neighbourhoods and the significance of these neighbourhoods. We evaluate ABIDE experimentally and demonstrate its strengths in performance against an argumentative baseline. |
| title | Argumentative Debates for Transparent Bias Detection [Technical Report] |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.04511 |