Fetching the paper…
Reading the bibliography…
We present a structural approach toward achieving equal opportunity in systems of algorithmic decision-making called algorithmic pluralism.
Multicalibration: Calibration for the (computationally-identifiable) masses. In International Conference on Machine Learning . PMLR, Vienna, Austria, 1939–1948
Ursula Hébert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum. 2018 · 1948
Earlier work this paper cites.
The Idea of Equality
Bernard Williams. 1962 · 1962
Earlier work this paper cites.
Select Issues: Assessing Adverse Impact in Software, Algorithms, and Artificial Intelligence Used in Employment Selection Procedures Under Title VII of the Civil Rights Act of 1964
US Equal Employment Opportunity Commission. 2023 · 1964
Earlier work this paper cites.
Questions and Answers to Clarify and Provide a Common Interpretation of the Uniform Guidelines on Employee Selection Procedures
US Equal Employment Opportunity Commission. 1979 · 1979
Earlier work this paper cites.
Equality of What?
Amartya Sen. 1980 · 1980
Earlier work this paper cites.
Making all the difference: Inclusion, exclusion, and American law
Martha Minow. 1990 · 1990
Earlier work this paper cites.
What Is the Point of Equality?
Elizabeth S. Anderson. 1999 · 1999
Earlier work this paper cites.
A theory of justice
John Rawls. 2004 · 2004
Earlier work this paper cites.
Building classifiers with independency constraints. In 2009 IEEE international conference on data mining workshops . IEEE, IEEE, 13–18
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy. 2009 · 2009
Earlier work this paper cites.
Bottlenecks: A new theory of equal opportunity
Joseph Fishkin. 2014 · 2014
Earlier work this paper cites.
The anti-oligarchy constitution
Joseph Fishkin and William E Forbath. 2014 · 2014
Earlier work this paper cites.
On the relation between accuracy and fairness in binary classification
Indre Zliobaite. 2015 · 2015
Earlier work this paper cites.
A computer program used for bail and sentencing decisions was labeled biased against blacks. It’s actually not that clear
Sam Corbett-Davies, Emma Pierson, Avi Feller, and Sharad Goel. 2016 · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro. 2016 · 2016
Earlier work this paper cites.
Inherent trade-offs in the fair determination of risk scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2016 · 2016
Earlier work this paper cites.
Indirect discrimination and the duty to avoid compounding injustice
Deborah Hellman. 2018 · 2017
Earlier work this paper cites.
Weapons of math destruction: How big data increases inequality and threatens democracy
Cathy O’Neil. 2017 · 2017
Earlier work this paper cites.
On Fairness and Calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger. 2017 · 2017
Earlier work this paper cites.
Design justice, AI, and escape from the matrix of domination
Sasha Costanza-Chock. 2018 · 2018
Earlier work this paper cites.
Logic-The theory of inquiry
John Dewey. 2018 · 2018
Earlier work this paper cites.
Amazon scraps secret AI recruiting tool that showed bias against women
Reuters. 2018 · 2018
Cited alongside, same era.
Automated employment discrimination
Ifeoma Ajunwa. 2019 · 2019
Cited alongside, same era.
Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse
Anna Lauren Hoffmann. 2019 · 2019
Cited alongside, same era.
Explaining explanations in AI. In Proceedings of the Conference on Fairness, Accountability, and Transparency . ACM, Atlanta, GA, 279–288
Brent Mittelstadt, Chris Russell, and Sandra Wachter. 2019 · 2019
Cited alongside, same era.
Dissecting racial bias in an algorithm used to manage the health of populations
Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019 · 2019
Cited alongside, same era.
LinkedIn’s job-matching AI was biased. The company’s solution? More AI
MIT Technology Review. 2021 · 2021
Later among the works it cites.
Fairness in ranking under uncertainty. In Advances in Neural Information Processing Systems , Vol. 34. Springer, Virtual, 11896–11908
Ashudeep Singh, David Kempe, and Thorsten Joachims. 2021 · 2021
Later among the works it cites.
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
Falaah Arif Khan, Eleni Manis, and Julia Stoyanovich. 2022 · 2022
Later among the works it cites.
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
Emily Black, Manish Raghavan, and Solon Barocas. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Andrew D Selbst, Danah Boyd, Sorelle A Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019 · 2019
Cited alongside, same era.
“No more credit score”: Employer credit check bans and signal substitution
Joshua Ballance, Robert Clifford, and Daniel Shoag. 2020 · 2020
Cited alongside, same era.
Transparency in Complex Computational Systems
Kathleen A. Creel. 2020 · 2020
Cited alongside, same era.
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
Ben Green and Salomé Viljoen. 2020 · 2020
Cited alongside, same era.
Hiring as exploration
Danielle Li, Lindsey R Raymond, and Peter Bergman. 2020 · 2020
Cited alongside, same era.
Predictive multiplicity in classification. In International Conference on Machine Learning . PMLR, ACM, Virtual, 6765–6774
Charles Marx, Flavio Calmon, and Berk Ustun. 2020 · 2020
Cited alongside, same era.
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
Manish Raghavan, Solon Barocas, Jon Kleinberg, and Karen Levy. 2020 · 2020
Cited alongside, same era.
Picking on the same person: Does algorithmic monoculture lead to outcome homogenization?
Rishi Bommasani, Kathleen A Creel, Ananya Kumar, Dan Jurafsky, and Percy S Liang. 2022 · 2022
Later among the works it cites.
The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems
Kathleen Creel and Deborah Hellman. 2022 · 2022
Later among the works it cites.
Escaping the impossibility of fairness: From formal to substantive algorithmic fairness
Ben Green. 2022 · 2022
Later among the works it cites.
Backward baselines: Is your model predicting the past?
Moritz Hardt and Michael P Kim. 2022 · 2022
Later among the works it cites.
Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophy. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society . AAAI, Oxford, England, 349–356
Atoosa Kasirzadeh. 2022 · 2022
Later among the works it cites.
Governing algorithmic decisions: The role of decision importance and governance on perceived legitimacy of algorithmic decisions
Ari Waldman and Kirsten Martin. 2022 · 2022
Later among the works it cites.
Algorithmic legitimacy in clinical decision-making
Sune Holm. 2023 · 2023
Closest in time.
Inside HireVue’s acquisition of Modern Hire
Allie Nawrat. 2023 · 2023
Closest in time.
Monoculture in matching markets
Kenny Peng and Nikhil Garg. 2023 · 2023
Closest in time.
Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes. In Advances in Neural Information Processing Systems , A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine (Eds.), Vol. 36. Curran Associates, Inc., New Orleans, LA, 51178–51201
Connor Toups, Rishi Bommasani, Kathleen Creel, Sarah Bana, Dan Jurafsky, and Percy S Liang. 2023 · 2023
Closest in time.
Less Discriminatory Algorithms
Emily Black, John Logan Koepke, Pauline Kim, Solon Barocas, and Mingwei Hsu. 2024 · 2024
Closest in time.
Scarce Resource Allocations That Rely On Machine Learning Should Be Randomized. In International Conference on Machine Learning . PMLR, Vienna, Austria
Shomik Jain, Kathleen Creel, and Ashia Wilson. 2024 · 2024
Closest in time.
The Impact of Generative AI on Labor Market Matching
Justin Kaashoek, Manish Raghavan, and John J. Horton. 2024 · 2024
Closest in time.
Alleged Rent-Fixing of Apartments Nationwide Draws More Legal Scrutiny
Will Parker. 2024 · 2024
Closest in time.
Against predictive optimization: On the legitimacy of decision-making algorithms that optimize predictive accuracy
Angelina Wang, Sayash Kapoor, Solon Barocas, and Arvind Narayanan. 2024 · 2024
Closest in time.