为机器学习公平性选择合适度量指标提供实用指南
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning
- 基于12项标准构建决策流程图，指导按场景选公平性指标
- 强调不同上下文下需用不同度量，单一指标无法满足所有需求
- 适合政策制定者、开发者和研究者快速定位适用公平性方案
近期人工智能监管提案强调机器学习模型的公平性要求。然而，由于哲学、文化与政治背景差异，准确界定合适的公平性度量极具挑战。模型所处上下文不同，偏差渗入方式也各异，导致单一通用公平性指标难以适用。这一模糊性凸显了建立选择准则的重要性，尤其在监管要求日益严格的背景下。为此，我们提出一个决策流程图，用于指导选择上下文适配的公平性度量。该流程图基于12项标准构建，涵盖模型评估标准、模型选择标准及数据偏差考量。同时，本文综述了机器学习中的公平性文献，并将其与核心监管工具关联，旨在帮助政策制定者、AI开发者、研究人员及其他利益相关方恰当地应对公平性问题并符合相关法规要求。
原文摘要 · Abstract (English)
Recent regulatory proposals for artificial intelligence emphasize fairness requirements for machine learning models. However, precisely defining the appropriate measure of fairness is challenging due to philosophical, cultural and political contexts. Biases can infiltrate machine learning models in complex ways depending on the model's context, rendering a single common metric of fairness insufficient. This ambiguity highlights the need for criteria to guide the selection of context-aware measures, an issue of increasing importance given the proliferation of ever tighter regulatory requirements. To address this, we developed a flowchart to guide the selection of contextually appropriate fairness measures. Twelve criteria were used to formulate the flowchart. This included consideration of model assessment criteria, model selection criteria, and data bias. We also review fairness literature in the context of machine learning and link it to core regulatory instruments to assist policymakers, AI developers, researchers, and other stakeholders in appropriately addressing fairness concerns and complying with relevant regulatory requirements.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。