Algorithmic fairness and structural injustice: Insights from Feminist Political Philosophy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the local scope of the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness. I close by some reflections of directions for future research.
Original languageEnglish
Title of host publicationAIES '22
Subtitle of host publicationProceedings of the AAAI/ACM Conference on AI, Ethics, and Society
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages349-356
ISBN (Print)9781450392471
DOIs
Publication statusPublished - 27 Jul 2022

Keywords

  • ethics of artificial intelligence
  • ethical machine learning
  • distributive justice
  • algorithmic justice
  • algorithmic fairness
  • structural injustice
  • algorithmic bias
  • responsibility
  • political philosophy
  • feminist philosophy

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