论文标题
开发公平机器学习的哲学框架：算法勾结的经验教训
Developing a Philosophical Framework for Fair Machine Learning: Lessons From The Case of Algorithmic Collusion
论文作者
论文摘要
公平的机器学习研究主要关注导致歧视的分类任务。但是，随着机器学习算法在新的环境中应用，结果与目前研究的算法在质量上不同。机器学习中的现有研究范式，该范式开发了公平性的指标和定义，无法说明这些定性不同类型的不公正现象。一个例子是算法勾结和市场公平性的问题。算法勾结的负面后果会影响所有消费者，不仅会影响受保护阶层的特定成员。在此案例研究中，我为机器学习中的研究人员和从业人员提出了一个道德框架，试图开发和应用扩展到新领域的公平指标。这贡献将公平性的正式指标与专门范围的规范原则联系起来。这使公平指标能够反映出与歧视的不同关注点。我以提案的局限性结束，并讨论了未来研究的有前途的途径。
Fair machine learning research has been primarily concerned with classification tasks that result in discrimination. However, as machine learning algorithms are applied in new contexts the harms and injustices that result are qualitatively different than those presently studied. The existing research paradigm in machine learning which develops metrics and definitions of fairness cannot account for these qualitatively different types of injustice. One example of this is the problem of algorithmic collusion and market fairness. The negative consequences of algorithmic collusion affect all consumers, not only particular members of a protected class. Drawing on this case study, I propose an ethical framework for researchers and practitioners in machine learning seeking to develop and apply fairness metrics that extends to new domains. This contribution ties the development of formal metrics of fairness to specifically scoped normative principles. This enables fairness metrics to reflect different concerns from discrimination. I conclude with the limitations of my proposal and discuss promising avenues for future research.
