Designing AI Systems that Support Fairness Across Distributive, Procedural, and Interactional Justice Dimensions

Abstract: The need for the most fair AI systems has been overemphasized as the AI influence keeps growing and critical decisions, among others, states by the healthcare, finance, and human resources sectors, are made.

AI fairness is not only about the fair distribution of results but also it involves fair processes in which decisions are made and the features of the interactions between the AI system and users.

This article uses the concepts of organizational justice as a frame to explain the ways by which the design of an AI system could become a vehicle for: distributive justice (fair distribution of resources and results); procedural justice (decision, making process that is open and impartial); and interactional justice (communication that is respectful and empathetic). The conjunction of the three dimensions that the AI system can facilitate will make it possible for the latter to be more in line with human values and hence receive more trust, legitimacy, and acceptance from the stakeholders (Colquitt et al., 2013; Binns, 2018).

This paper also refers to the various ways which include bias mitigation techniques, algorithmic transparency, and user, centric interfaces that bring fairness into the system.

Further on, the authors explain the present continuous issues (for instance, data bias and ethical tradeoffs) and recommend future research directions for enhancing just AI systems at the end of this paper (Miller, 2017; Selbst et al., 2019).

Artificial Intelligence, Fairness, Organizational Justice, Distributive Justice, Procedural Justice, Interactional Justice

1. Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing [Google Scholar] [Crossref]

2. Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.15779/Z38BG31 [Google Scholar] [Crossref]

3. Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim Code. Polity Press. [Google Scholar] [Crossref]

4. Bies, R. J., & Moag, J. S. (1986). Interactional justice: Communication criteria of fairness. In R. J. Lewicki, B. H. Sheppard, & M. H. Bazerman (Eds.), Research on negotiation in organizations (Vol. 1, pp. 43–55). JAI Press. [Google Scholar] [Crossref]

5. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency (FAT), 149–159. https://doi.org/10.1145/3287560.3287583 [Google Scholar] [Crossref]

6. Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 5(2), 153–163. https://doi.org/10.1089/big.2016.0047 [Google Scholar] [Crossref]

7. Colquitt, J. A. (2001). On the dimensionality of organizational justice: A construct validation of a measure. Journal of Applied Psychology, 86(3), 386–400. https://doi.org/10.1037/0021-9010.86.3.386 [Google Scholar] [Crossref]

8. Dastin, J. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G [Google Scholar] [Crossref]

9. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608. https://doi.org/10.48550/arXiv.1702.08608 [Google Scholar] [Crossref]

10. Eiband, M., Schneider, H., Bilandzic, M., Fazekas-Con, T., Haug, M., & Hussmann, H. (2018). Bringing transparency design into practice. Proceedings of the 23rd International Conference on Intelligent User Interfaces (IUI '18), 211–223. https://doi.org/10.1145/3172944.3172961 [Google Scholar] [Crossref]

11. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press. [Google Scholar] [Crossref]

12. Friedler, S. A., Scheidegger, C., & Venkatasubramanian, S. (2019). The (im)possibility of fairness: Different value systems require different mechanisms for fair decision-making. Communications of the ACM, 62(4), 82–89. https://doi.org/10.1145/3287560 [Google Scholar] [Crossref]

13. Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems (TOIS), 14(3), 330–347. https://doi.org/10.1145/230538.230561 [Google Scholar] [Crossref]

14. Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2019). Predictably unequal? The effects of machine learning on credit markets. National Bureau of Economic Research. https://doi.org/10.3386/w25943 [Google Scholar] [Crossref]

15. Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a "right to explanation." AI Magazine, 38(3), 50–57. https://doi.org/10.1609/aimag.v38i3.2741 [Google Scholar] [Crossref]

16. Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems (NeurIPS), 29, 3315–3323. https://proceedings.neurips.cc/paper_files/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf [Google Scholar] [Crossref]

17. Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudik, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need? Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3290605.3300830 [Google Scholar] [Crossref]

18. Kim, P. T. (2017). Auditing algorithms for discrimination. University of Pennsylvania Law Review Online, 166, 189–203. https://scholarship.law.upenn.edu/penn_law_review_online/vol166/iss1/12/ [Google Scholar] [Crossref]

19. Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016). Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807. https://doi.org/10.48550/arXiv.1609.05807 [Google Scholar] [Crossref]

20. Lipton, Z. C. (2016). The mythos of model interpretability. arXiv preprint arXiv:1606.03490. https://doi.org/10.48550/arXiv.1606.03490 [Google Scholar] [Crossref]

21. Miller, T. (2017). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. https://doi.org/10.1016/j.artint.2018.07.007 [Google Scholar] [Crossref]

22. Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81–103. https://doi.org/10.1111/0022-4537.00153 [Google Scholar] [Crossref]

23. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342 [Google Scholar] [Crossref]

24. Picard, R. W. (1997). Affective computing. MIT Press. [Google Scholar] [Crossref]

25. Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., ... & Wellman, M. (2019). Machine behaviour. Nature, 568(7753), 477–486. https://doi.org/10.1038/s41586-019-1138-y [Google Scholar] [Crossref]

26. Raji, I. D., Smart, A., White, R., Mitchell, M., Gebru, T., Hutchinson, B., ... & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT), 33–44. https://doi.org/10.1145/3351095.3372873 [Google Scholar] [Crossref]

27. Turkle, S. (2011). Alone together: Why we expect more from technology and less from each other. Basic Books. [Google Scholar] [Crossref]

28. Verma, S., & Rubin, J. (2018). Fairness definitions explained. 2018 IEEE/ACM International Workshop on Software Fairness (FairWare), 1–7. https://doi.org/10.1145/3194770.3194776 [Google Scholar] [Crossref]

29. Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99. https://doi.org/10.1093/idpl/ipx005 [Google Scholar] [Crossref]

30. West, S. M., Whittaker, M., & Crawford, K. (2019). Discriminating systems: Gender, race and power in AI. AI Now Institute. https://ainowinstitute.org/discriminatingsystems.pdf [Google Scholar] [Crossref]

31. Whittaker, M., Crawford, K., Dobbe, R., Fried, G., Kaziunas, E., Mathur, V., ... & Schwartz, O. (2018). AI Now Report 2018. AI Now Institute. https://ainowinstitute.org/AI_Now_2018_Report.pdf [Google Scholar] [Crossref]

• Competency and Challenges of BTLED-ICT Students in 2D Animation: An Analytical Study
• Slope Stability Assessment: A Case Study of Embankments Along OMU-Aran-Ilorin Road, Nigeria
• Advancements in Precursors, Materials, Deposition Techniques for Thin Film Research in Electronic Devices: A Mini Review
• “Empowering Indian Women through Entrepreneurship: A Study on Kolkata”
• Impact of Mental Mathematics Proficiency on Job Performance Among Seconadry Schools Teachers in Emohua and Port Hacourt City