论文标题
机器学习中的互惠
Reciprocity in Machine Learning
论文作者
论文摘要
机器学习无处不在。它通过预测睡眠方式或疾病风险的模型来为Spotify，Instagram和YouTube以及医疗保健系统等推荐系统提供动力。个人向这些模型贡献数据，并从中受益。这些贡献（影响力流出）和益处（影响力的流入）是相互的吗？我们建议对先前提出的培训数据影响措施进行流出，流入和互惠的措施。我们最初的理论和经验结果表明，在某些分布假设下，某些类别的模型大约是相互的。我们以几个开放的方向结束。
Machine learning is pervasive. It powers recommender systems such as Spotify, Instagram and YouTube, and health-care systems via models that predict sleep patterns, or the risk of disease. Individuals contribute data to these models and benefit from them. Are these contributions (outflows of influence) and benefits (inflows of influence) reciprocal? We propose measures of outflows, inflows and reciprocity building on previously proposed measures of training data influence. Our initial theoretical and empirical results indicate that under certain distributional assumptions, some classes of models are approximately reciprocal. We conclude with several open directions.
