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Fairness in Relational Domains

Published: 27 December 2018 Publication History

Abstract

AI and machine learning tools are being used with increasing frequency for decision making in domains that affect peoples' lives such as employment, education, policing and loan approval. These uses raise concerns about biases of algorithmic discrimination and have motivated the development of fairness-aware machine learning. However, existing fairness approaches are based solely on attributes of individuals. In many cases, discrimination is much more complex, and taking into account the social, organizational, and other connections between individuals is important. We introduce new notions of fairness that are able to capture the relational structure in a domain. We use first-order logic to provide a flexible and expressive language for specifying complex relational patterns of discrimination. Furthermore, we extend an existing statistical relational learning framework, probabilistic soft logic (PSL), to incorporate our definition of relational fairness. We refer to this fairness-aware framework FairPSL. FairPSL makes use of the logical definitions of fairnesss but also supports a probabilistic interpretation. In particular, we show how to perform maximum a posteriori(MAP) inference by exploiting probabilistic dependencies within the domain while avoiding violation of fairness guarantees. Preliminary empirical evaluation shows that we are able to make both accurate and fair decisions.

References

[1]
European Union Legislation. (a) Racial Equality Directive. 2000. (b) Employment Equality Directive, 2000. (c) Gender Employment Directive, 2006. (d) Equal Treatment Directive (proposal), 2008.
[2]
UK Legislation. (a) Sex Discrimination Act. 1975. (b) Race Relation Act, 1976.
[3]
United Nations Legislation. (a) Universal Declaration of Human Rights. 1948. (c) Convention on the Elimination of All forms of Racial Discrimination, 1966. (d) Convention on the Elimination of All forms of Discrimination Against Women, 1979.
[4]
Duhai Alshukaili, Alvaro A. A. Fernandes, and Norman W. Paton. 2016. Structuring Linked Data Search Results Using Probabilistic Soft Logic. In International Semantic Web Conference (1) (Lecture Notes in Computer Science), Vol. 9981. 3--19.
[5]
Stephen H. Bach, Matthias Broecheler, Bert Huang, and Lise Getoor. 2017. Hinge- Loss Markov Random Fields and Probabilistic Soft Logic. Journal of Machine Learning Research 18 (2017), 109:1--109:67.
[6]
Solon Barocas and Andrew D Selbst. 2016. Big data's disparate impact. California Law Review 104 (2016), 671.
[7]
Danah Boyd, Karen Levy, and Alice Marwick. 2014. The networked nature of algorithmic discrimination. In Data and discrimination: Collected essays. 53--57.
[8]
YooJung Choi, Adnan Darwiche, and Guy Van den Broeck. 2017. Optimal Feature Selection for Decision Robustness in Bayesian Networks. In IJCAI. ijcai.org, 1554--1560.
[9]
Alexandra Chouldechova. 2017. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. CoRR abs/1703.00056 (2017).
[10]
Steven Diamond and Stephen P. Boyd. 2016. CVXPY: A Python-Embedded Modeling Language for Convex Optimization. Journal of Machine Learning Research 17 (2016), 83:1--83:5.
[11]
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel. 2012. Fairness through awareness. In ITCS. ACM, 214--226.
[12]
Javid Ebrahimi, Dejing Dou, and Daniel Lowd. 2016. Weakly Supervised Tweet Stance Classification by Relational Bootstrapping. In EMNLP. The Association for Computational Linguistics, 1012--1017.
[13]
Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. 2015. Certifying and Removing Disparate Impact. In KDD. ACM, 259--268.
[14]
Lise Getoor and Ben Taskar. 2007. Introduction to Statistical Relational Learning. Vol. 1. MIT press Cambridge.
[15]
Moritz Hardt, Eric Price, and Nati Srebro. 2016. Equality of Opportunity in Supervised Learning. In NIPS. 3315--3323.
[16]
Toshihiro Kamishima, Shotaro Akaho, and Jun Sakuma. 2011. Fairness-aware Learning through Regularization Approach. In ICDMW. IEEE Computer Society, 643--650.
[17]
Pigi Kouki, Shobeir Fakhraei, James R. Foulds, Magdalini Eirinaki, and Lise Getoor. 2015. HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems. In RecSys. ACM, 99--106.
[18]
Matt J. Kusner, Joshua R. Loftus, Chris Russell, and Ricardo Silva. 2017. Counterfactual Fairness. In NIPS. 4069--4079.
[19]
Dino Pedreschi, Salvatore Ruggieri, and Franco Turini. 2012. A study of top-k measures for discrimination discovery. In SAC. ACM, 126--131.
[20]
Dino Pedreschi, Salvatore Ruggieri, and Franco Turini. 2013. The Discovery of Discrimination. In Discrimination and Privacy in the Information Society. Studies in Applied Philosophy, Epistemology and Rational Ethics, Vol. 3. Springer, 91--108.
[21]
Dhanya Sridhar, Shobeir Fakhraei, and Lise Getoor. 2016. A probabilistic approach for collective similarity-based drug-drug interaction prediction. Bioinformatics 32, 20 (2016), 3175--3182.
[22]
RobertWest, Hristo S. Paskov, Jure Leskovec, and Christopher Potts. 2014. Exploiting Social Network Structure for Person-to-Person Sentiment Analysis. TACL 2 (2014), 297--310.
[23]
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez-Rodriguez, Krishna P. Gummadi, and Adrian Weller. 2017. From Parity to Preference-based Notions of Fairness in Classification. In NIPS. 228--238.
[24]
Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork. 2013. Learning Fair Representations. In ICML (3) (JMLRWorkshop and Conference Proceedings), Vol. 28. JMLR.org, 325--333.

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  • (2025)Fairness for machine learning software in education: A systematic mapping studyJournal of Systems and Software10.1016/j.jss.2024.112244219(112244)Online publication date: Jan-2025
  • (2025)FAIREDU: A multiple regression-based method for enhancing fairness in machine learning models for educational applicationsExpert Systems with Applications10.1016/j.eswa.2024.126219269(126219)Online publication date: Apr-2025
  • (2024)Artificial intelligence in talent acquisition: exploring organisational and operational dimensionsInternational Journal of Organizational Analysis10.1108/IJOA-09-2023-399232:11(108-131)Online publication date: 1-Jul-2024
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cover image ACM Conferences
AIES '18: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society
December 2018
406 pages
ISBN:9781450360128
DOI:10.1145/3278721
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 December 2018

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Author Tags

  1. fairness
  2. probabilistic soft logic
  3. statistical relational learning

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  • Research-article

Funding Sources

  • IVADO
  • The National Science Foundation

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AIES '18
Sponsor:
AIES '18: AAAI/ACM Conference on AI, Ethics, and Society
February 2 - 3, 2018
LA, New Orleans, USA

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AIES '18 Paper Acceptance Rate 61 of 162 submissions, 38%;
Overall Acceptance Rate 61 of 162 submissions, 38%

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Cited By

View all
  • (2025)Fairness for machine learning software in education: A systematic mapping studyJournal of Systems and Software10.1016/j.jss.2024.112244219(112244)Online publication date: Jan-2025
  • (2025)FAIREDU: A multiple regression-based method for enhancing fairness in machine learning models for educational applicationsExpert Systems with Applications10.1016/j.eswa.2024.126219269(126219)Online publication date: Apr-2025
  • (2024)Artificial intelligence in talent acquisition: exploring organisational and operational dimensionsInternational Journal of Organizational Analysis10.1108/IJOA-09-2023-399232:11(108-131)Online publication date: 1-Jul-2024
  • (2023)Fic Graph - Quantifying Fairness with Sensitive AttributeSSRN Electronic Journal10.2139/ssrn.4352107Online publication date: 2023
  • (2023)Bias Mitigation for Machine Learning Classifiers: A Comprehensive SurveyACM Journal on Responsible Computing10.1145/3631326Online publication date: 1-Nov-2023
  • (2023)Toward A Logical Theory Of Fairness and BiasTheory and Practice of Logic Programming10.1017/S1471068423000157(1-19)Online publication date: 19-Jul-2023
  • (2023)Knowledge representation and acquisition for ethical AI: challenges and opportunitiesEthics and Information Technology10.1007/s10676-023-09692-z25:1Online publication date: 11-Mar-2023
  • (2023)Algorithmic discrimination in the credit domain: what do we know about it?AI & SOCIETY10.1007/s00146-023-01676-339:4(2059-2098)Online publication date: 17-May-2023
  • (2022)Trade less Accuracy for Fairness and Trade-off Explanation for GNN2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020318(4681-4690)Online publication date: 17-Dec-2022
  • (2022)Tractable Probabilistic Models for Ethical AIGraph-Based Representation and Reasoning10.1007/978-3-031-16663-1_1(3-8)Online publication date: 11-Sep-2022
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