Fairness and Abstraction in Sociotechnical Systems

A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science---such as abstraction and modular design---are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We…

• NS
  National Science Foundation

  Awards: IIS-1633400, IIS-1633387, IIS-1633724, 1633400, 1633724, IIS-1633400, 1633387, IIS-1633724