在算法公平性和可解释性中对部分信息分解的审查
Sanghamitra Dutta1, Faisal Hamman1
1Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
部分信息分解 (PID) 量化了随机变量中的信息. 本综述探讨了PID在机器学习应用程序的算法公平性和可解释性方面的新兴角色.
科学领域:
- 信息理论 信息理论
- 机器学习 机器学习
- 算法公平性 算法公平性
- 可以解释的可解释性.
背景情况:
- 部分信息分解 (PID) 量化了变量之间共享的信息.
- 机器学习越来越多地用于高风险的应用程序,需要公平性和可解释性.
- 现有的方法缺乏对信息贡献的细微量化.
研究的目的:
- 在算法公平性和可解释性方面调查PID的最新和新兴应用.
- 在这些领域引入PID应用的分类学.
- 审查PID估计技术和未来的方向.
主要方法:
- 在算法公平性和可解释性方面对PID应用的文献综述.
- PID角色的分类学分类:量化差异,解释贡献,并正式化权衡.
- 讨论PID估计技术.
主要成果:
- 通过使用因果关系,PID能够解开算法公平性的非豁免差异.
- 在联合学习中,PID量化了地方和全球差异之间的权衡.
- 一个分类法强调了PID在审计,特征解释和联合学习中的实用性.
结论:
- PID提供了强大的工具来增强算法的公平性和可解释性.
- 在PID估计和应用开发方面需要进一步的研究.
- 在高风险领域,PID对于负责任的AI至关重要.
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