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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Learning Optimal Fair Policies.

Razieh Nabi1, Daniel Malinsky1, Ilya Shpitser1

  • 1Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.

Proceedings of Machine Learning Research
|December 31, 2019
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Summary
This summary is machine-generated.

This study introduces a novel method to create fair automated decisions by correcting for societal biases in data. The approach uses causal inference and optimization to ensure fairness constraints are met, breaking cycles of injustice.

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Area of Science:

  • Computer Science
  • Statistics
  • Social Science

Background:

  • Societal biases in data collection and definition perpetuate injustice.
  • Automated decision-making algorithms can amplify existing societal unfairness.
  • Fairness in algorithmic decision-making is crucial for equitable policy implementation.

Purpose of the Study:

  • To develop a method for making optimal and fair decisions.
  • To correct for unfair dependence on sensitive attributes (e.g., race, gender).
  • To break the cycle of injustice perpetuated by biased data and algorithms.

Main Methods:

  • Utilizing methods from causal inference.
  • Applying constrained optimization techniques.
  • Extending the Nabi & Shpitser (2018) approach for policy learning.

Main Results:

  • A theoretical guarantee that the learned fair policy satisfies fairness constraints.
  • Demonstration of the approach with synthetic and real-world criminal justice data.
  • Correction for multiple potential biases in sensitive data contexts.

Conclusions:

  • The proposed method effectively learns optimal policies that ensure fairness.
  • This approach offers a pathway to mitigate bias in automated decision-making.
  • The findings have implications for fair policy implementation in sensitive domains.