Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
117 tokens/sec
GPT-4o
8 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Algorithmic Pluralism: A Structural Approach To Equal Opportunity (2305.08157v4)

Published 14 May 2023 in cs.CY

Abstract: We present a structural approach toward achieving equal opportunity in systems of algorithmic decision-making called algorithmic pluralism. Algorithmic pluralism describes a state of affairs in which no set of algorithms severely limits access to opportunity, allowing individuals the freedom to pursue a diverse range of life paths. To argue for algorithmic pluralism, we adopt Joseph Fishkin's theory of bottlenecks, which focuses on the structure of decision-points that determine how opportunities are allocated. The theory contends that each decision-point or bottleneck limits access to opportunities with some degree of severity and legitimacy. We extend Fishkin's structural viewpoint and use it to reframe existing systemic concerns about equal opportunity in algorithmic decision-making, such as patterned inequality and algorithmic monoculture. In proposing algorithmic pluralism, we argue for the urgent priority of alleviating severe bottlenecks in algorithmic decision-making. We contend that there must be a pluralism of opportunity available to many different individuals in order to promote equal opportunity in a systemic way. We further show how this framework has several implications for system design and regulation through current debates about equal opportunity in algorithmic hiring.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (51)
  1. Ifeoma Ajunwa. 2021. An Auditing Imperative for Automated Hiring Systems. Harvard Journal of Law & Technology 34, 2 (2021).
  2. Elizabeth S. Anderson. 1999. What Is the Point of Equality? Ethics 109, 2 (Jan. 1999), 287–337. https://doi.org/10.1086/233897
  3. Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines. In Equity and Access in Algorithms, Mechanisms, and Optimization (Arlington, VA, USA) (EAAMO ’22). Association for Computing Machinery, New York, NY, USA, Article 18, 10 pages. https://doi.org/10.1145/3551624.3555303
  4. “No more credit score”: Employer credit check bans and signal substitution. Labour Economics 63 (2020), 101769.
  5. Model Multiplicity: Opportunities, Concerns, and Solutions. In 2022 ACM Conference on Fairness, Accountability, and Transparency (Seoul, Republic of Korea) (FAccT ’22). Association for Computing Machinery, New York, NY, USA, 850–863. https://doi.org/10.1145/3531146.3533149
  6. Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 3663–3678. https://proceedings.neurips.cc/paper_files/paper/2022/file/17a234c91f746d9625a75cf8a8731ee2-Paper-Conference.pdf
  7. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021).
  8. Building classifiers with independency constraints. In 2009 IEEE international conference on data mining workshops. IEEE, 13–18.
  9. US Equal Employment Opportunity Commission. 1979. Questions and Answers to Clarify and Provide a Common Interpretation of the Uniform Guidelines on Employee Selection Procedures.
  10. A computer program used for bail and sentencing decisions was labeled biased against blacks. It’s actually not that clear. Washington Post 17 (2016).
  11. Sasha Costanza-Chock. 2018. Design justice, AI, and escape from the matrix of domination. Journal of Design and Science 3, 5 (2018).
  12. Kathleen Creel and Deborah Hellman. 2022. The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems. Canadian Journal of Philosophy 52, 1 (2022), 26–43. https://doi.org/10.1017/can.2022.3
  13. Kathleen A. Creel. 2020. Transparency in Complex Computational Systems. Philosophy of Science 87, 4 (Oct. 2020), 568–589. https://doi.org/10.1086/709729
  14. Algorithmic reparation. Big Data & Society 8, 2 (2021), 20539517211044808.
  15. John Dewey. 1938. Logic: The Theory of Inquiry. (1938).
  16. Benjamin Eidelson. 2021. Patterned Inequality, Compounding Injustice, and Algorithmic Prediction. American Journal of Law and Equality 1 (2021), 252–276.
  17. Joseph Fishkin. 2014. Bottlenecks: A new theory of equal opportunity. Oxford University Press, USA.
  18. Joseph Fishkin and William E Forbath. 2014. The anti-oligarchy constitution. BUL Rev. 94 (2014), 669.
  19. The (im) possibility of fairness: Different value systems require different mechanisms for fair decision making. Commun. ACM 64, 4 (2021), 136–143.
  20. Ben Green. 2022. Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness. Philosophy and Technology 35 (12 2022). Issue 4.
  21. Ben Green and Salomé Viljoen. 2020. Algorithmic Realism: Expanding the Boundaries of Algorithmic Thought. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* ’20). Association for Computing Machinery, New York, NY, USA, 19–31. https://doi.org/10.1145/3351095.3372840
  22. Mario Günther and Atoosa Kasirzadeh. 2021. Algorithmic and human decision making: for a double standard of transparency. AI & SOCIETY 37, 1 (April 2021), 375–381. https://doi.org/10.1007/s00146-021-01200-5
  23. Moritz Hardt and Michael P Kim. 2022. Backward baselines: Is your model predicting the past? arXiv preprint arXiv:2206.11673 (2022).
  24. Equality of opportunity in supervised learning. Advances in neural information processing systems 29 (2016).
  25. Multicalibration: Calibration for the (Computationally-Identifiable) Masses. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80), Jennifer Dy and Andreas Krause (Eds.). PMLR, 1939–1948. https://proceedings.mlr.press/v80/hebert-johnson18a.html
  26. Deborah Hellman. 2018. Indirect discrimination and the duty to avoid compounding injustice. Foundations of Indirect Discrimination Law, Hart Publishing Company (2018), 2017–53.
  27. Anna Lauren Hoffmann. 2019. Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse. Information, Communication & Society 22, 7 (2019), 900–915.
  28. Sune Holm. 2023. Algorithmic legitimacy in clinical decision-making. Ethics and Information Technology 25, 3 (July 2023). https://doi.org/10.1007/s10676-023-09709-7
  29. Atoosa Kasirzadeh. 2022. Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophy. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society. 349–356.
  30. Maximilian Kasy and Rediet Abebe. 2021. Fairness, equality, and power in algorithmic decision-making. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 576–586.
  31. Inherent trade-offs in the fair determination of risk scores. arXiv preprint arXiv:1609.05807 (2016).
  32. Jon Kleinberg and Manish Raghavan. 2021a. Algorithmic monoculture and social welfare. Proceedings of the National Academy of Sciences 118, 22 (2021), e2018340118. https://doi.org/10.1073/pnas.2018340118 arXiv:https://www.pnas.org/doi/pdf/10.1073/pnas.2018340118
  33. Jon Kleinberg and Manish Raghavan. 2021b. Algorithmic monoculture and social welfare. Proceedings of the National Academy of Sciences 118, 22 (2021), e2018340118.
  34. Hiring as exploration. Technical Report. National Bureau of Economic Research.
  35. Martha Minow. 1990. Making all the difference: Inclusion, exclusion, and American law. Cornell University Press.
  36. Explaining Explanations in AI. In Proceedings of the Conference on Fairness, Accountability, and Transparency. ACM. https://doi.org/10.1145/3287560.3287574
  37. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 (2019), 447–453. https://doi.org/10.1126/science.aax2342 arXiv:https://www.science.org/doi/pdf/10.1126/science.aax2342
  38. Cathy O’Neil. 2017. Weapons of math destruction: How big data increases inequality and threatens democracy. Crown.
  39. On Fairness and Calibration. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc.
  40. Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (Barcelona, Spain) (FAT* ’20). Association for Computing Machinery, New York, NY, USA, 469–481. https://doi.org/10.1145/3351095.3372828
  41. John Rawls. 2004. A theory of justice. In Ethics. Routledge, 229–234.
  42. Reuters. 2018. Amazon scraps secret AI recruiting tool that showed bias against women.
  43. MIT Technology Review. 2021. LinkedIn’s job-matching AI was biased. The company’s solution? More AI. https://www.technologyreview.com/2021/06/23/1026825/linkedin-ai-bias-ziprecruiter-monster-artificial-intelligence/.
  44. Fairness and abstraction in sociotechnical systems. In Proceedings of the conference on fairness, accountability, and transparency. 59–68.
  45. Amartya Sen. 1980. Equality of What? Cambridge University Press, Cambridge.
  46. Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes. arXiv preprint arXiv:2307.05862 (2023).
  47. Suresh Venkatasubramanian and Mark Alfano. 2020. The philosophical basis of algorithmic recourse. In Proceedings of the 2020 conference on fairness, accountability, and transparency. 284–293.
  48. Ari Waldman and Kirsten Martin. 2022. Governing algorithmic decisions: The role of decision importance and governance on perceived legitimacy of algorithmic decisions. Big Data & Society 9, 1 (Jan. 2022), 205395172211004. https://doi.org/10.1177/20539517221100449
  49. Against Predictive Optimization. In 2023 ACM Conference on Fairness, Accountability, and Transparency. ACM. https://doi.org/10.1145/3593013.3594030
  50. Bernard Williams. 1962. The Idea of Equality. In Philosophy, Politics and Society, Peter Laslett and Walter Runciman (Eds.). Blackwell, 112–117.
  51. Indre Zliobaite. 2015. On the relation between accuracy and fairness in binary classification. arXiv preprint arXiv:1505.05723 (2015).
Citations (6)

Summary

We haven't generated a summary for this paper yet.