Abstract
Discrimination-aware data mining is expected to play an important role in data-driven decision making, as “BIG data” can be obtained from the actual society. To build the appropriate decision making system, AI researchers and practitioners have proposed various discrimination measures. However, most of the existing discrimination measures cannot be interpreted as the “proportion” and thus may not provide the comparable evaluation of the discrimination level. To evaluate how much of discrimination is based on a sensitive feature directly, indirectly, or totally, we propose three proportion measures of discrimination using natural direct and indirect effects [12]. The effectiveness of the proposed discrimination measures is confirmed on Adult Census Data [2].
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We would like to thank the two anonymous reviewers for their helpful comments.
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This research was funded by JFE Engineering Corporation and Japan Society for the Promotion of Science (JSPS), Grant Number 19K11856 and 21H03504.
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Shingaki, R., Kuroki, M. (2024). New Proportion Measures of Discrimination Based on Natural Direct and Indirect Effects. In: Barneva, R.P., Brimkov, V.E., Gentile, C., Pacchiano, A. (eds) Artificial Intelligence and Image Analysis. IWCIA ISAIM 2024 2024. Lecture Notes in Computer Science, vol 14494. Springer, Cham. https://doi.org/10.1007/978-3-031-63735-3_10
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