饼:通过将现成模型结合起来,实现多维人工智能公平性的框架
Yi Sheng1, Junhuan Yang1, Lei Yang1
1George Mason University.
概括
解决人工智能模型偏见至关重要. 本研究介绍了Muffin,这是一个框架,可以同时提高多个属性的公平性,克服单个属性的公平性解决方案的局限性.
科学领域:
- 人工智能的人工智能
- 机器学习伦理学 机器学习伦理学
- 算法公平性 算法公平性
背景情况:
- 模型公平性或偏见是人工智能应用中的一个关键问题,例如自动驾驶和医疗保健.
- 现有的研究主要针对单个不公平的属性,忽视了多个属性的现实数据中的多维公平性.
- 不同的不公平属性之间经常存在强烈的相关性,其中优化一个可能会对其他属性产生负面影响.
研究的目的:
- 研究人工智能模型中多维公平性的挑战.
- 提出一个新的框架,Muffin,同时提高公平在多个属性.
- 在Muffin中开发一个自动工具,将现有模型结合起来,以提高多属性公平性.
主要方法:
- 在多维数据集中分析不同不公平属性之间的相关性.
- 开发了Muffin框架,这是一个用于统一现成模型的自动工具.
- 用皮肤病学数据集的案例研究来评估Muffin的性能与现有方法相比.
主要成果:
- 现有的方法在一个属性上显示了有限的公平性改善,而在另一个属性上造成了不公平性 (例如,属性1上的21.05%,属性2上的1.85%).
- 拟议的Muffin框架在两个属性上实现了26.32%和20.37%的同时公平性改进.
- Muffin还导致模型准确度提高了5.58%.
结论:
- 优化一个属性的公平性可能会导致其他属性的公平性崩.
- 马芬框架有效地解决了多维公平问题,通过将模型结合起来,同时在多个属性上提高公平性.
- 马芬为在各种现实应用中开发更公平的AI系统提供了一个有前途的解决方案.
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