تقرير
Fairness Auditing with Multi-Agent Collaboration
| العنوان: | Fairness Auditing with Multi-Agent Collaboration |
|---|---|
| المؤلفون: | de Vos, Martijn, Dhasade, Akash, Bourrée, Jade Garcia, Kermarrec, Anne-Marie, Merrer, Erwan Le, Rottembourg, Benoit, Tredan, Gilles |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | Existing work in fairness auditing assumes that each audit is performed independently. In this paper, we consider multiple agents working together, each auditing the same platform for different tasks. Agents have two levers: their collaboration strategy, with or without coordination beforehand, and their strategy for sampling appropriate data points. We theoretically compare the interplay of these levers. Our main findings are that (i) collaboration is generally beneficial for accurate audits, (ii) basic sampling methods often prove to be effective, and (iii) counter-intuitively, extensive coordination on queries often deteriorates audits accuracy as the number of agents increases. Experiments on three large datasets confirm our theoretical results. Our findings motivate collaboration during fairness audits of platforms that use ML models for decision-making. Comment: 13 pages, 6 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2402.08522 |
| رقم الأكسشن: | edsarx.2402.08522 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |