Algorithmic Pluralism A Structural Approach Towards Equal Opportunity

While the idea of equal opportunity enjoys a broad consensus, many disagree about what it means for opportunities to be equal. The algorithmic fairness community often relies on formal approaches to quantitatively determine if opportunities are allocated equally. A more structural approach put forth by Joseph Fishkin focuses on the wider network of decisions that determine which opportunities are allocated to whom. In this so-called opportunity structure, decision points represent bottlenecks that are often chained together so that the output of one decision is an input to the next. By evaluating the severity and legitimacy of these bottlenecks, Fishkin offers a qualitative framework to assess whether equal opportunity is infringed upon in a structural way. We adopt this structural viewpoint and use it to reframe many interdisciplinary discussions about equal opportunity in systems of algorithmic decision-making. Drawing on examples from education, healthcare, and criminal justice, we recommend prioritizing regulatory and design-based interventions that alleviate severe bottlenecks in order to help expand access to opportunities in a pluralistic way.

[1]  Kathleen A. Creel,et al.  Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? , 2022, NeurIPS.

[2]  Rohan Taori,et al.  Data Feedback Loops: Model-driven Amplification of Dataset Biases , 2022, ICML.

[3]  Falaah Arif Khan,et al.  Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrines , 2022, EAAMO.

[4]  Michael P. Kim,et al.  Backward baselines: Is your model predicting the past? , 2022, ArXiv.

[5]  Solon Barocas,et al.  Model Multiplicity: Opportunities, Concerns, and Solutions , 2022, FAccT.

[6]  A. Kasirzadeh Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophy , 2022, AIES.

[7]  Michaela Slussareff O'Neil, Cathy. 2016. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy . Crown. , 2022, CyberOrient.

[8]  A. Chouldechova,et al.  Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness , 2022, FAccT.

[9]  William E. Forbath,et al.  The Anti-Oligarchy Constitution , 2022 .

[10]  Michael N. Bastedo,et al.  What If We Leave It Up to Chance? Admissions Lotteries and Equitable Access at Selective Colleges , 2021, Educational Researcher.

[11]  Iason Gabriel Toward a Theory of Justice for Artificial Intelligence , 2021, Daedalus.

[12]  D. Wesson,et al.  Introducing a Special Series: Addressing Racial and Ethnic Disparities in Kidney Disease. , 2021, Journal of the American Society of Nephrology : JASN.

[13]  N. Powe,et al.  A Unifying Approach for GFR Estimation: Recommendations of the NKF-ASN Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease. , 2021, Journal of the American Society of Nephrology : JASN.

[14]  Emma Brunskill,et al.  Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making , 2021, ArXiv.

[15]  Benjamin Eidelson PATTERNED INEQUALITY, COMPOUNDING INJUSTICE, AND ALGORITHMIC PREDICTION , 2021, American Journal of Law and Equality.

[16]  Michael S. Bernstein,et al.  On the Opportunities and Risks of Foundation Models , 2021, ArXiv.

[17]  Ben Green Escaping the Impossibility of Fairness: From Formal to Substantive Algorithmic Fairness , 2021, Philosophy & Technology.

[18]  Jenny L. Davis,et al.  Algorithmic reparation , 2021, Big Data Soc..

[19]  B. Hedden On statistical criteria of algorithmic fairness , 2021 .

[20]  Kathleen Creel,et al.  The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems , 2021, Canadian Journal of Philosophy.

[21]  Rediet Abebe,et al.  Fairness, Equality, and Power in Algorithmic Decision-Making , 2021, FAccT.

[22]  Abeba Birhane,et al.  Algorithmic injustice: a relational ethics approach , 2021, Patterns.

[23]  Jon Kleinberg,et al.  Algorithmic monoculture and social welfare , 2021, Proceedings of the National Academy of Sciences.

[24]  M. Mendu,et al.  Examining the Potential Impact of Race Multiplier Utilization in Estimated Glomerular Filtration Rate Calculation on African-American Care Outcomes , 2020, Journal of General Internal Medicine.

[25]  Celestine Mendler-Dünner,et al.  Stochastic Optimization for Performative Prediction , 2020, NeurIPS.

[26]  Josef Coresh,et al.  Kidney Disease, Race, and GFR Estimation. , 2020, Clinical journal of the American Society of Nephrology : CJASN.

[27]  Celestine Mendler-Dünner,et al.  Performative Prediction , 2020, ICML.

[28]  Ben Green,et al.  Algorithmic realism: expanding the boundaries of algorithmic thought , 2020, FAT*.

[29]  Mark Alfano,et al.  The philosophical basis of algorithmic recourse , 2020, FAT*.

[30]  Brian W. Powers,et al.  Dissecting racial bias in an algorithm used to manage the health of populations , 2019, Science.

[31]  J. Kleinberg,et al.  Mitigating bias in algorithmic hiring: evaluating claims and practices , 2019, FAT*.

[32]  A. Hoffmann Where fairness fails: data, algorithms, and the limits of antidiscrimination discourse , 2019, Information, Communication & Society.

[33]  Danah Boyd,et al.  Fairness and Abstraction in Sociotechnical Systems , 2019, FAT.

[34]  J. Lash,et al.  CKD and ESRD in US Hispanics. , 2019, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[35]  Inioluwa Deborah Raji,et al.  Model Cards for Model Reporting , 2018, FAT.

[36]  Sandra G. Mayson Bias In, Bias Out , 2018 .

[37]  K. Rodvold,et al.  Renal Dosing of Antibiotics: Are We Jumping the Gun? , 2018, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[38]  Krishna P. Gummadi,et al.  A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity , 2018, ArXiv.

[39]  Sharad Goel,et al.  The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning , 2018, ArXiv.

[40]  Sasha Costanza-Chock Design Justice, A.I., and Escape from the Matrix of Domination , 2018, Journal of Design and Science.

[41]  Percy Liang,et al.  Fairness Without Demographics in Repeated Loss Minimization , 2018, ICML.

[42]  Timnit Gebru,et al.  Datasheets for datasets , 2018, Commun. ACM.

[43]  Tony Doyle,et al.  Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy , 2017, Inf. Soc..

[44]  Deborah Hellman,et al.  Indirect Discrimination and the Duty to Avoid Compounding Injustice , 2017 .

[45]  Seth Neel,et al.  Rawlsian Fairness for Machine Learning , 2016, ArXiv.

[46]  Nathan Srebro,et al.  Equality of Opportunity in Supervised Learning , 2016, NIPS.

[47]  Jon M. Kleinberg,et al.  Inherent Trade-Offs in the Fair Determination of Risk Scores , 2016, ITCS.

[48]  Hugh Lazenby Bottlenecks: A New Theory of Equal Opportunity , 2016 .

[49]  D. Shoag,et al.  'No More Credit Score': Employer Credit Check Bans and Signal Substitution , 2016 .

[50]  Indre Zliobaite,et al.  On the relation between accuracy and fairness in binary classification , 2015, ArXiv.

[51]  Raffaella Sette,et al.  Mass incarceration: the whole pie , 2015 .

[52]  Toon Calders,et al.  Building Classifiers with Independency Constraints , 2009, 2009 IEEE International Conference on Data Mining Workshops.

[53]  C. Schmid,et al.  A new equation to estimate glomerular filtration rate. , 2009, Annals of internal medicine.

[54]  Teva J. Scheer Uniform Guidelines on Employee Selection Procedures , 2007 .

[55]  A. Levey,et al.  A More Accurate Method To Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation , 1999, Annals of Internal Medicine.

[56]  Elizabeth Anderson,et al.  What Is the Point of Equality? , 1999, Ethics.

[57]  Guy Haarscher The idea of equality , 1982 .

[58]  J. Rawls,et al.  A Theory of Justice , 1971, Princeton Readings in Political Thought.

[59]  D. Acemoglu,et al.  How AI Fails Us , 2021 .

[60]  J. Stockman,et al.  A New Equation to Estimate Glomerular Filtration Rate , 2011 .

[61]  Keith C. Norris,et al.  Chronic kidney disease in the United States: a public policy imperative. , 2008, Clinical journal of the American Society of Nephrology : CJASN.

[62]  Pamela Kelly,et al.  SUPREME COURT OF WISCONSIN , 2005 .

[63]  S. Buetow,et al.  Power Issues in the Doctor-Patient Relationship , 2004, Health Care Analysis.

[64]  Martha Minow,et al.  Making All the Difference: Inclusion, Exclusion, and American Law , 1990 .

[65]  J. Dewey Logic, the theory of inquiry , 1938 .