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Fair pattern discovery

Published: 24 March 2014 Publication History

Abstract

Data mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. We are assisting to unprecedented opportunities of understanding human and society behavior that unfortunately is darkened by several risks for human rights: one of this is the unfair discrimination based on the extracted patterns and profiles. Consider the case when a set of patterns extracted from the personal data of a population of individual persons is released for subsequent use in a decision making process, such as, e.g., granting or denying credit. Decision rules based on such patterns may lead to unfair discrimination, depending on what is represented in the training cases. In this context, we address the discrimination risks resulting from publishing frequent patterns. We present a set of pattern sanitization methods, one for each discrimination measure used in the legal literature, for fair (discrimination-protected) publishing of frequent pattern mining results. Our proposed pattern sanitization methods yield discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion. Finally, the effectiveness of our proposals is assessed by extensive experiments.

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Cited By

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  • (2021)A Review of Gender Bias Mitigation in Credit Scoring Models2021 Ethics and Explainability for Responsible Data Science (EE-RDS)10.1109/EE-RDS53766.2021.9708589(1-10)Online publication date: 27-Oct-2021
  • (2019)A Framework for Benchmarking Discrimination-Aware Models in Machine LearningProceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3306618.3314262(437-444)Online publication date: 27-Jan-2019

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  1. Fair pattern discovery

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    cover image ACM Conferences
    SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
    March 2014
    1890 pages
    ISBN:9781450324694
    DOI:10.1145/2554850
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 24 March 2014

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    Author Tags

    1. anti-discrimination
    2. data mining
    3. frequent pattern discovery

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    SAC 2014: Symposium on Applied Computing
    March 24 - 28, 2014
    Gyeongju, Republic of Korea

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    SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
    Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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    View all
    • (2021)A Review of Gender Bias Mitigation in Credit Scoring Models2021 Ethics and Explainability for Responsible Data Science (EE-RDS)10.1109/EE-RDS53766.2021.9708589(1-10)Online publication date: 27-Oct-2021
    • (2019)A Framework for Benchmarking Discrimination-Aware Models in Machine LearningProceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3306618.3314262(437-444)Online publication date: 27-Jan-2019

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