2019/05/13 by Anna Lauren Hoffmann · 2 voices · 17 citations
Social Sciences · #Digital Economy and Work Transformation #Ethics and Social Impacts of AI #Privacy, Security, and Data Protection
paper · doi:10.1080/1369118x.2019.1573912
openalex publication_date 2019/05/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/18
Problems of bias and fairness are central to data justice, as they speak directly to the threat that ‘big data’ and algorithmic decision-making may worsen already existing injustices. In the United States, grappling with these problems has found clearest expression through liberal discourses of rights, due process, and antidiscrimination. Work in this area, however, has tended to overlook certain established limits of antidiscrimination discourses for bringing about the change demanded by social justice. In this paper, I engage three of these limits: 1) an overemphasis on discrete ‘bad actors’, 2) single-axis thinking that centers disadvantage, and 3) an inordinate focus on a limited set of goods. I show that, in mirroring some of antidiscrimination discourse’s most problematic tendencies, efforts to achieve fairness and combat algorithmic discrimination fail to address the very hierarchical logic that produces advantaged and disadvantaged subjects in the first place. Finally, I conclude by sketching three paths for future work to better account for the structural conditions against which we come to understand problems of data and unjust discrimination in the first place.