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“Un”Fair Machine Learning Algorithms

Ensuring fairness in algorithmic decision making is a crucial policy issue. Current legislation ensures fairness by barring algorithm designers from using demographic information in their decision making. As a result, to be legally compliant, the algorithms need to ensure equal treatment. However, in many cases, ensuring equal treatment leads to disparate impact particularly when there are differences among groups based on demographic classes. In response, several “fair” machine learning (ML) algorithms that require impact parity (e.g., equal opportunity) at the cost of equal treatment have recently been proposed to adjust for the societal inequalities. Advocates of fair ML propose changing the law to allow the use of protected class-specific decision rules. We show that the proposed fair ML algorithms that require impact parity, while conceptually appealing, can make everyone worse off, including the very class they aim to protect. Compared with the current law, which requires treatment parity, the fair ML algorithms, which require impact parity, limit the benefits of a more accurate algorithm for a firm. As a result, profit maximizing firms could underinvest in learning, that is, improving the accuracy of their machine learning algorithms. We show that the investment in learning decreases when misclassification is costly, which is exactly the case when greater accuracy is otherwise desired. Our paper highlights the importance of considering strategic behavior of stake holders when developing and evaluating fair ML algorithms. Overall, our results indicate that fair ML algorithms that require impact parity, if turned into law, may not be able to deliver some of the anticipated benefits.

Ketersediaan

Informasi Detail

Judul Seri

MANAGEMENT SCIENCE; Vol.68 No.6 June 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 4173-4195

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

ALGORITHMIC BIAS
ECONOMICS OF ARTIFICIAL INTELLIGENCE
FAIR MACHINE LEARNING
EQUAL IMPACT
EQUAL TREATMENT

Info Detail Spesifik

https://doi.org/10.1287/mnsc.2021.4065

Pernyataan Tanggungjawab

Runshan Fu, Manmohan Aseri, Param Vir Singh, Kannan Srinivasan

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