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Bias and Fairness in Data-Based Decision Making

  • PHILOSOPHY·THOUGHT·CULTURE
  • 2022, (38), pp.1~24
  • DOI : 10.33639/ptc.2022..38.001
  • Publisher : Research Institute for East-West Thought
  • Research Area : Humanities > Other Humanities
  • Received : December 30, 2021
  • Accepted : January 31, 2022
  • Published : January 31, 2022

Ko, Insok 1

1인하대학교

Accredited

ABSTRACT

In this paper I investigate the problem of bias that threatens the rationality of data-based decision-making in the data-driven society we are entering, and seek ways to minimize that threat and to enhance the fairness of such decision-making. With this purpose in mind, I examine recent criticism about COMPAS that has been used for criminal correctional management in several states in U.S.A. COMPAS has been criticized that it (re-)produces biases in the evaluation of the criminals that varies depending on the criminal's skin color. I analyze two sources of such biases, the dataset and the algorithm. This analysis reveals that the data are not neutral image that reflect reality as it is, but product of measurements that embodies the perspectives of data-producers. Such intervention from specific perspective does not in itself endangers the fairness of data-driven decision-making. However, the bias in which the properties of the sample data deviate from those of the population reduces the effectiveness of data-based decision-making and thus undermines its core value. Therefore, a data-driven society should keep an eye on the biases in data-based decisions, perform regular assessments and revisions of the data processing algorithms and supplement the dataset itself coping with the result of the assessments.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.