Targeted Learning for Data Fairness

Concerns about the use of data and algorithms for decision-making have risen in concert with the ascendancy of data-driven methods. Prior research has investigated problems ranging from privacy violations to explainability and accountability [10] [12]. Here, we focus on fairness, which addresses discrimination and disparate treatment in data and algorithms [1]. Recently, fairness research has focused significantly on algorithmic fairness, which aims to investigate algorithms for unfairness and intervene on algorithms to prevent unfair outcomes. Prior work has developed metrics for fairness, methods for fair variable selection, and mechanisms to correct unfair models [14] [11]. Algorithmic fairness evolved out of concerns about the use of algorithms in high-stakes contexts, particularly those that have a history of disparity and discrimination. When training an algorithm on data from a discriminatory process, it is common for the algorithm to learn to replicate that discrimination, or'garbage in, garbage out'. Much work in algorithmic fairness focuses on detecting, evaluating, and correcting unfair algorithms under a garbage-in-garbage-out regime. However, little work focuses on assessing the extent to which the data-generating process itself is'garbage',