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The Journal of Financial Data Science

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Fairness Measures for Machine Learning in Finance

Sanjiv Das, Michele Donini, Jason Gelman, Kevin Haas, Mila Hardt, Jared Katzman, Krishnaram Kenthapadi, Pedro Larroy, Pinar Yilmaz and Muhammad Bilal Zafar
The Journal of Financial Data Science Fall 2021, jfds.2021.1.075; DOI: https://doi.org/10.3905/jfds.2021.1.075
Sanjiv Das
is a professor of finance at Santa Clara University and an Amazon scholar at Amazon Web Services in Santa Clara, CA
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Michele Donini
is a senior applied scientist at Amazon Web Services in Berlin, Germany
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Jason Gelman
is a principal product manager, technical, at Amazon Web Services in Santa Clara, CA
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Kevin Haas
is a senior manager, software, at Amazon Web Services in Santa Clara, CA
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Mila Hardt
is a senior software development engineer at Amazon Web Services in Santa Clara, CA
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Jared Katzman
is a research assistant with the Computational Social Science Group at Microsoft Research in New York, NY
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Krishnaram Kenthapadi
is a principal scientist at Amazon Web Services in Santa Clara, CA
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Pedro Larroy
is a software development engineer at Amazon Web Services in Santa Clara, CA
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Pinar Yilmaz
is a senior software development engineer at Amazon Web Services in Santa Clara, CA
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Muhammad Bilal Zafar
is an applied scientist at Amazon Web Services in Berlin, Germany
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Abstract

The authors present a machine learning pipeline for fairness-aware machine learning (FAML) in finance that encompasses metrics for fairness (and accuracy). Whereas accuracy metrics are well understood and the principal ones are used frequently, there is no consensus as to which of several available measures for fairness should be used in a generic manner in the financial services industry. The authors explore these measures and discuss which ones to focus on at various stages in the ML pipeline, pre-training and post-training, and they examine simple bias mitigation approaches. Using a standard dataset, they show that the sequencing in their FAML pipeline offers a cogent approach to arriving at a fair and accurate ML model. The authors discuss the intersection of bias metrics with legal considerations in the United States, and the entanglement of explainability and fairness is exemplified in the case study. They discuss possible approaches for training ML models while satisfying constraints imposed from various fairness metrics and the role of causality in assessing fairness.

TOPICS: Big data/machine learning, legal/regulatory/public policy, performance measurement

Key Findings

  • ▪ Sources of bias are presented and a range of metrics is considered for machine learning applications in finance, both pre-training and post-training of models.

  • ▪ A process of using the metrics to arrive at fair models is discussed.

  • ▪ Various considerations for the choice of specific metrics are also analyzed.

  • © 2021 Pageant Media Ltd
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The Journal of Financial Data Science: 4 (2)
The Journal of Financial Data Science
Vol. 4, Issue 2
Spring 2022
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Fairness Measures for Machine Learning in Finance
Sanjiv Das, Michele Donini, Jason Gelman, Kevin Haas, Mila Hardt, Jared Katzman, Krishnaram Kenthapadi, Pedro Larroy, Pinar Yilmaz, Muhammad Bilal Zafar
The Journal of Financial Data Science Sep 2021, jfds.2021.1.075; DOI: 10.3905/jfds.2021.1.075

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Fairness Measures for Machine Learning in Finance
Sanjiv Das, Michele Donini, Jason Gelman, Kevin Haas, Mila Hardt, Jared Katzman, Krishnaram Kenthapadi, Pedro Larroy, Pinar Yilmaz, Muhammad Bilal Zafar
The Journal of Financial Data Science Sep 2021, jfds.2021.1.075; DOI: 10.3905/jfds.2021.1.075
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  • Article
    • Abstract
    • BACKGROUND: ALGORITHMIC BIAS AND FINANCE
    • BIAS METRICS
    • NONBINARY ATTRIBUTES AND LABELS
    • BIAS MITIGATION
    • SEQUENCING FAML IN PRACTICE: A CASE STUDY
    • ISSUES IN FINANCIAL FAIRNESS
    • CONCLUDING DISCUSSION
    • ENDNOTES
    • REFERENCES
  • Info & Metrics
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  • PDF (Subscribers Only)

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