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On Consequentialism and Fairness.

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Summary
This summary is machine-generated.

This study critiques machine learning fairness by applying consequentialism, an ethical framework focusing on outcomes. It highlights tradeoffs in fairness definitions and automated decision-making systems.

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Area of Science:

  • Machine Learning Ethics
  • Algorithmic Fairness
  • Consequentialist Ethics

Background:

  • Existing machine learning fairness research prioritizes outcome definitions and quantification.
  • Ethical foundations underpinning fairness efforts receive limited attention.
  • Consequentialism, an outcome-focused ethical theory, offers a critical lens.

Purpose of the Study:

  • To provide a consequentialist critique of current machine learning fairness definitions.
  • To offer a machine learning perspective on consequentialism.
  • To explore ethical implications of automated decision-making systems.

Main Methods:

  • Applying consequentialist ethical principles to analyze machine learning fairness metrics.
  • Examining the challenges of uncertainty, subjectivity, and aggregation in consequentialist decision-making.
  • Reviewing machine learning approaches to consequentialism.

Main Results:

  • Consequentialism provides a robust framework for critiquing existing machine learning fairness literature.
  • Identifies key tradeoffs: defining who counts, policy utility, and long-term value.
  • Highlights the need for a machine learning perspective on consequentialist decision theory.

Conclusions:

  • Consequentialism offers a powerful foundation for evaluating machine learning fairness.
  • Automated decision-making systems necessitate careful consideration of learning and randomization.
  • Future work should integrate ethical frameworks like consequentialism into AI development.