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AUC IURIDICA, Vol 70 No 2 (2024), 85–99
Procedural Fairness as Stepping Stone for Successful Implementation of Algorithmic Decision-Making in Public Administration: Review and Outlook
Sven Hoeppner, Martin Samek
DOI: https://doi.org/10.14712/23366478.2024.24
zveřejněno: 23. 05. 2024
Abstract
Algorithmic decision-making (ADM) is becoming more and more prevalent in everyday life. Due to their promise of producing faster, better, and less biased decisions, automated and data-driven processes also receive increasing attention in many different administrative settings. However, as a result of human mistakes ADM also poses the threat of producing unfair outcomes. Looming algorithmic discrimination can undermine the legitimacy of administrative decision-making. While lawyers and lawmakers face the age-old question of regulation, many decision-makers tasked with designing ADM for and implementing ADM in public administration wrestle with harnessing its advantages and limiting its disadvantages. “Algorithmic fairness” has evolved as key concept in developing algorithmic systems to counter detrimental outcomes. We provide a review of the vast literature on algorithmic fairness and show how key dimensions alter people’s perception of whether an algorithm is fair. In doing so, we provide entry point into this literature for anybody who is required to think about algorithmic fairness, particularly in an public administration context. We also pinpoint critical concerns about algorithmic fairness that public officials and researchers should note.
klíčová slova: algorithmic decision-making; administration; procedural fairness; artificial intelligence
reference (70)
1. ACIGKOZ, Y. - DAVIDSON, K. H. - COMPAGNONE, M. et. al. Justice perceptions of artificial intelligence in selection. International Journal of Selection and Assessment. 2020, Vol. 28, No. 3, pp. 399-416. CrossRef
2. ALBACH, M. - WRIGHT, J. R. The Role of Accuracy in Algorithmic Process Fairness Across Multiple Domains. In: Proceedings of the ACM Conference on Economics and Computation. New York: Association for Computing Machinery, 2021, pp. 29-49. CrossRef
3. AlgorithmWatch. Automating Society: Taking Stock of Automated Decision-Making in the EU [online]. Bertelsmann Stiftung, 2019 [cit. 2023-12-14]. Available at: https://algorithmwatch.org/de/wp-content/uploads/2019/02/Automating_Society_Report_2019.pdf.
4. Artificial Intelligence Act: deal on comprehensive rules for trustworthy AI. In: News: European Parliament: Press releases [online]. 9.12.2023 [cit. 2024-01-29]. Available at: https://www.europarl.europa.eu/news/en/press-room/20231206IPR15699/artificial-intelligence-act-deal-on-comprehensive-rules-for-trustworthy-ai.
5. BANKINS, S. - FORMOSA, P. - GRIEP, Y. - RICHARDS, D. AI decision making with dignity? Contrasting workers' justice perceptions of human and AI decision making in a human resource management context. Information Systems Frontiers. 2022, Vol. 3, No. 2, pp. 1-19. CrossRef
6. BARABAS, CH. et al. Studying Up: Reorienting the study of algorithmic fairness around issues of power. In: Proceedings of the ACM Conference on Fairness Accountability, and Transparency. New York: Association for Computing Machinery, 2020, pp. 167-176. CrossRef
7. BAROCAS, S. - SELBST, A. D. Big data's disparate impact. California Law Review. 2016, Vol. 104, No. 1, pp. 671-729. CrossRef
8. BINNS, R. Fairness in machine learning: Lessons from political philosophy. Proceedings of Machine Learning Research. 2018, No. 81, pp. 149-159.
9. BINNS, R. What can political philosophy teach us about algorithmic fairness? IEEE Security & Privacy. 2018, Vol. 16, No. 3, pp. 73-80. CrossRef
10. BINNS, R. - VAN KLEEK, M. - VEALE, M. - LYNGS, U. - ZHAO, J. - SHADBOLT, N. It's Reducing Human Being to a Percentage: Perceptions of Justice in Algorithmic Decisions. In: Proceedings of the CHI Conference on Human Factors in Computing Systems. New York: Association for Computing Machinery, 2018, pp. 1-14. CrossRef
11. BOLTON, G. E. - BRANDTS, J. - OCKENFELS, A. Fair procedures: evidence from games involving lotteries. The Economic Journal. 2005, Vol. 115, No. 506, pp. 1054-1076. CrossRef
12. COGLIANESE, C. Administrative Law in the Automated State. Daedalus. 2021, Vol. 150, No. 3, pp. 104-120. CrossRef
13. Commission welcomes political agreement on Artificial Intelligence Act. In: European Commission: Press release [online]. 11.12.2023 [cit. 2024-01-29]. Available at: https://digital-strategy.ec.europa.eu/en/news/commission-welcomes-political-agreement-artificial-intelligence-act.
14. DODGE, J. - LIAO, V. Q. - ZHANG, Y. - BELLAMY, R. K. E. - DUGAN, C. Explaining models: An empirical study of how explanations impact fairness judgment. In: Proceedings of the International Conference on Intelligent User Interfaces. New York: Association for Computing Machinery, 2019, pp. 275-285. CrossRef
15. Ethics guidelines for trustworthy AI. In: European Commission: Shaping Europe's digital future [online]. 8.4.2019 [cit. 2024-01-08]. Available at: https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai.
16. EUBANKS, V. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. New York: St Martin's Press, 2018.
17. Explanatory memorandum, article 3.5 of Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act), COM(2021) 206 final, 2021/0106(COD).
18. GAJANE, P. - PECHENIZKIY, M. On Formalizing Fairness in Prediction with Machine Learning. In: arXiv [online]. 2017 [cit. 2023-12-13] Available at: http://arxiv.org/abs/1710.03184.
19. GERDON, F. - BACH, R. L. - KERN, CH. - KREUTER, F. Social impacts of algorithmic decision-making: a research agenda for the social sciences. Big Data & Society. 2022, Vol. 9, No. 1, pp. 1-13. CrossRef
20. GRGIĆ-HLAČA, N. - ZAFAR, M. B. - GUMMADI, K. P. - WELLER, A. Beyond Distributive Fairness in Algorithmic Decision Making: Feature Selection for Procedurally Fair Learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. New York: Association for Computing Machinery, 2018, pp. 51-60. CrossRef
21. HANNAN, J. - CHEN, H.-Y. W. - JOSEPH, K. Who Gets What According to Whom? An Analysis of Fairness Perceptions in Service Allocation. In: Proceedings of the 2021 AAAI/ACMConference on AI, Ethics, and Society. New York: Association for Computing Machinery, 2021, pp. 555-565. CrossRef
22. HARRISON, G. - HANSON, J. - JACINTO, CH. - RAMIREZ, J. - UR, B. An Empirical Study on the Perceived Fairness of Realistic, Imperfect Machine Learning Models. In: Proceedings of the ACM Conference on Fairness, Accountability, and Transparency. New York: Association for Computing Machinery, 2020, pp. 392-402. CrossRef
23. HIRSCH, T. - MERCED, K. - NARAYANAN, S. - IMEL, Z. E. - ATKINS, D. C. Designing contestability: Interaction design, machine learning, and mental health. In: Proceedings of the 2017 Conference on Designing Interactive Systems. New York: Association for Computing Machinery, 2017, pp. 95-99. CrossRef
24. HOULDEN, P. - LATOUR, S. - WALKER, L. - THIBAUT, J. Preference for modes of dispute resolution as a function of process and decision control. Journal of Experimental Social Psychology. 1978, Vol. 14, No. 1, pp. 13-30. CrossRef
25. HSU, S. - LI, T. W. - ZHANG, Z. - FOWLER, M. - ZILLES, C. - KARAHALIOS, K. Attitudes Surrounding an Imperfect AI Autograder. In: Proceedings of the CHI Conference on Human Factors in Computing Systems. New York: Association for Computing Machinery, 2021, pp. 1-15. CrossRef
26. HUTCHINSON, B. - MITCHELL, M. 50 years of test (un)fairness: lessons for machine learning. In: FAT* '19: Proceedings of the ACM Conference on Fairness, Accountability, and Transparency. New York: Association for Computing Machinery 2019, pp. 49-58. CrossRef
27. CHENG, H.-F. - STAPLETON, L. - WANG, R. - BULLOCK, P. - CHOULDECHOVA, A. - WU, Z. S. S. - ZHU, H. Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. New York: Association for Computing Machinery, 2021, pp. 1-17. CrossRef
28. CHOULDECHOVA, A. Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big Data. Vol. 5, No. 2, pp. 153-163. CrossRef
29. JOBIN, A. - IENCA, M. - VAVERNA, E. The global landscape of AI ethics guidelines. Nature Machine Intelligence. 2019, Vol. 1, No. 9, pp. 389-399. CrossRef
30. KIESLICH, K. - KELLER, B. - STARKE, CH. Artificial intelligence ethics by design: evaluating public perception on the importance of ethical design principles of artificial intelligence. Big Data & Society. 2022, Vol. 9, No. 1, pp. 1-19. CrossRef
31. KLEINBERG, J. - LUDWIG, J. - MULLAINATHAN, S. - RAMBACHAN, A. Algorithmic fairness. AEA papers and proceedings. 2018, Vol. 108, pp. 22-27. CrossRef
32. KÖCHLING, A. - WEHNER, M. C. Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Business Research. 2020, Vol. 13, No. 3, pp. 1-54. CrossRef
33. KORDZADEH, N. - GHASEMAGEI, M. Algorithmic bias: review, synthesis, and future research directions. European Journal of Information Systems. 2022, Vol. 31, No. 3, pp. 388-409. CrossRef
34. KURZ, V. - ORLAND, A. - POSADZY, K. Fairness versus efficiency: how procedural fairness concerns affect coordination. Experimental Economics. 2018, Vol. 21, pp. 601-626. CrossRef
35. LEE, M. K. Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society. 2018, Vol. 5, No. 1 pp. 1-16. CrossRef
36. LEE, M. K. - RICH, K. Who Is Included in Human Perceptions of AI?: Trust and Perceived Fairness around Healthcare AI and Cultural Mistrust. In: Proceedings of the CHI Conference on Human Factors in Computing Systems. New York: Association for Computing Machinery, 2021, pp. 1-14. CrossRef
37. LEE, M. K. - JAIN, A. - CHA, H. J. - OJHA, S. - KUSBIT, D. Procedural justice in algorithmic fairness: Leveraging transparency and outcome control for fair algorithmic mediation. Proceedings of the ACM on Human-Computer Interaction. 2019, Vol. 3, No. CSCW, Art. 182, pp. 1-26. CrossRef
38. LEPRI, B. et al. Fair, Transparent, and Accountable Algorithmic Decision-making Processes: The Premise, the Proposed Solutions, and the Open Challenges. Philosophy and Technology. 2018, Vol. 31, No. 4, pp. 611-627. CrossRef
39. LEVENTHAL, G. S. What should be done with equity theory? In: GERGEN, K. J. - GREENBERG, M. S. - WILLIS, R. H. (eds.). Social exchange: Advances in Theory and Research. Boston: Springer, 1976, pp. 27-55. CrossRef
40. LIND, A. E. - LISAK, R. I. - CONLON, D. E. Decision Control and Process Control Effects on Procedural Fairness Judgments 1. Journal of Applied Social Psychology. 1983, Vol. 13, No. 4, pp. 338-350. CrossRef
41. LIU, X. - LORINI, E. - ROTOLO, A. - SARTOR, G. Modelling and Explaining Legal Case-based Reasoners through Classifiers. Frontiers in Artificial Intelligence and Applications, in corso di stampa. 2022, pp. 1-13. CrossRef
42. MÜHLHOFF, R. Predictive privacy: Collective data protection in the context of artificial intelligence and big data. Big Data & Society [online]. 2023, Vol. 10, No. 1 [cit. 2023-01-04]. CrossRef
43. NAM, T. Do the right thing right! Understanding the hopes and hypes of data-based policy. Government Information Quarterly. 2020, Vol. 37, No. 3, pp. 1-10. CrossRef
44. NYARKO, J. - GOEL, S. - SOMMERS, R. Breaking Taboos in Fair Machine Learning: An Experimental Study. In: Proceedings of Equity and Access in Algorithms, Mechanisms, and Optimization. New York: Association for Computing Machinery, 2021, pp. 1-11. CrossRef
45. Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act), COM(2021) 206 final, 2021/0106(COD).
46. Recommendation CM/Rec(2007)7 of the Committee of Ministers to member states on good administration.
47. Recommendation of the Council on Artificial Intelligence. In: OECD Legal Instruments [online]. [cit. 2024-01-08]. Availabe at: https://legalinstruments.oecd.org/en/instruments/oecd-legal-0449.
48. Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (GDPR).
49. RAHWAN, I. Society-in-the-loop: programming the algorithmic social contract. Ethics and Information Technology. 2018, Vol 20, No. 1, pp. 5-14. CrossRef
50. RICHARDSON, R. et. al. Litigating Algorithms 2019 US Report: New Challenges to Government Use of Algorithmic Decision Systems. In: AINow [online]. 17.9.2029 [cit. 2024-01-02]. Available at: https://ainowinstitute.org/publication/litigating-algorithms-2019-u-s-report-2.
51. SELBST, A. D. - POWLES, J. Meaningful information and the right to explanation. International Data Privacy Law. 2017, Vol. 7, No. 4, pp. 233-242. Available online in: Oxford Academy [online]. [cit. 2024-02-21]. CrossRef
52. SHIN, D. - PARK, Y. J. Role of fairness, accountability, and transparency in algorithmic affordance. Computers in Human Behavior. 2019, Vol. 98, No. C, pp. 277-284. CrossRef
53. SHULNER-TAL, A. - KUFLIK, T. - KLIGER, D. Fairness, explainability and in-between: Understanding the impact of different explanation methods on non-expert users' perceptions of fairness toward an algorithmic system. Ethics and Information Technology. 2022, Vol. 24, No. 1, pp. 1-13. CrossRef
54. SCHLICKER, N. - LANGER, M. - ÖTTING, S. K. - BAUM, K. - KÖNIG, C. J. - WALLACH, D. What to expect from opening up 'black boxes'? Comparing perceptions of justice between human and automated agents. Computers in Human Behavior. 2021, Vol. 122, No. 4, pp. 1-16. CrossRef
55. SCHREPEL, T. Law and Technology Realism. MIT Computational Law Report [online]. 2020 [cit. 2024-01-03]. Available at: https://law.mit.edu/pub/lawandtechnologyrealism/release/3.
56. SLOANE, M. - MOSS, E. AI's social sciences deficit. Nature Machine Intelligence. 2019, Vol. 1, No. 8, pp. 330-331. CrossRef
57. SMITH, J. - SONBOLI, N. - FIESLER, C. - BURKE, R. Exploring User Opinions of Fairness in Recommender Systems. In: CHI'20 Workshop on Human-Centered Approaches to Fair and Responsible AI. New York: Association for Computing Machinery, 2020, pp. 1-4.
58. SRIVASTAVA, M. - HEIDARI, H. - KRAUSE, A. Mathematical notions vs. Human perception of fairness: A descriptive approach to fairness for machine learning. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery, 2019, pp. 2459-2468. CrossRef
59. STARKE, CH. - BALEIS, J. - KELLER, B. - MARCINKOWSKI, F. Fairness perceptions of algorithmic decision-making: a systematic review of the empirical literature. Big Data & Society. 2022, Vol. 9, No. 2, pp. 1-16. CrossRef
60. SUN, L. - TANG, Y. Data-Driven discrimination, perceived fairness, and consumer trust: the perspective of consumer attribution. Frontiers in Psychology. 2021, No. 12, pp. 1-13. CrossRef
61. THIBAUT, J. W. - WALKER, L. Procedural justice: a psychological analysis. New York: L. Erlbaum Associates, 1975.
62. TRAUTMANN, S. T. Procedural fairness and equality of opportunity. Journal of Economic Surveys. 2023, Vol. 37, No. 5, pp. 1697-1714. CrossRef
63. VEALE, M. - BINNS, R. Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. Big Data & Society. 2017, Vol. 4, No. 2, pp. 1-17. CrossRef
64. VEALE, M. - BORGESIUS, F. Z. Demystifying the Draft EU Artificial Intelligence Act: analysing the good, the bad, and the unclear elements of the proposed approach. Computer Law Review International. Vol. 22, No. 4, pp. 97-112. CrossRef
65. VERMA, S. - RUBIN, J. Fairness Definitions Explained. In: Proceedings of the International Workshop on Software Fairness. New York: Association for Computing Machinery, 2018, pp. 1-7. CrossRef
66. WANG, A. J. Procedural Justice and Risk-Assessment Algorithms. In: SSRN [online]. 2018 [cit. 2024-01-04]. Available at: https://ssrn.com/abstract=3170136. CrossRef
67. WANG, R. - HARPER, M. F. - ZHU, H. Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences. In: Proceedings of the CHI Conference on Human Factors in Computing Systems. New York: Association for Computing Machinery, 2020, pp. 1-14. CrossRef
68. WANG, X. - ZHANG, Y. - ZHU, R. A brief review on algorithmic fairness. Management System Engineering. 2022, Vol. 1, No. 7, pp. 1-13. CrossRef
69. ZHOU, J. - VERMA, S. - MITTAL, M. - CHEN, F. Understanding Relations Between Perception of Fairness and Trust in Algorithmic Decision Making. In: Proceedings of the International Conference on Behavioral and Social Computing (BESC 2021). IEEE, 2021, pp. 1-5. CrossRef
70. ŽLIOBAITĖ, I. Measuring discrimination in algorithmic decision making. Data Mining and Knowledge Discovery. 2017, Vol. 31, No. 4, pp. 1060-1089. CrossRef
Procedural Fairness as Stepping Stone for Successful Implementation of Algorithmic Decision-Making in Public Administration: Review and Outlook is licensed under a Creative Commons Attribution 4.0 International License.
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ISSN: 0323-0619
E-ISSN: 2336-6478
