Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants | Paper Citation Record

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This paper cites The child opportunity index: improving collaboration between community development and public health.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants The child opportunity index: improving collaboration between community development and public health

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This paper cites A reductions approach to fair classification.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants A reductions approach to fair classification

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Fair regression: Quantitative definitions and reduction-based algorithms

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants The social psychology of discrimination: Theory, measurement and consequences

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants What makes wrongful discrimination wrong? biases, preferences, stereotypes, and proxies

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This paper cites The New Jim Crow: Mass Incarceration in the Age of Colorblindness.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants The New Jim Crow: Mass Incarceration in the Age of Colorblindness

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This paper cites Racial/ethnic differences in physician distrust in the United States.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Racial/ethnic differences in physician distrust in the United States

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This paper cites Grades are not normal: Improving exam score models using the logit-normal distribution.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Grades are not normal: Improving exam score models using the logit-normal distribution

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This paper cites Rényi fair inference.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Rényi fair inference

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Observation 4dc1eb7c-c108-4cec-b0ef-2436b4416f42 · outbound

This paper cites Fairness and Machine Learning: Limitations and Opportunities.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Fairness and Machine Learning: Limitations and Opportunities

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Unresolved cited work

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This paper cites Inequality and Heterogeneity: A primitive Theory of Social Structure, volume 7.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Inequality and Heterogeneity: A primitive Theory of Social Structure, volume 7

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This paper cites Distinction: A Social Critique of the Judgement of Taste.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Distinction: A Social Critique of the Judgement of Taste

Reference 13

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This paper cites The social determinants of health: It's time to consider the causes of the causes.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants The social determinants of health: It's time to consider the causes of the causes

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This paper cites Socioeconomic status in health research: One size does not fit all.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Socioeconomic status in health research: One size does not fit all

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Causally interpreting intersectionality theory

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Building classifiers with independency constraints

Reference 17

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This paper cites Distributional assumptions in educational assessments analysis: Normal distributions versus generalized beta distribution in modeling the phenomenon of learning.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Distributional assumptions in educational assessments analysis: Normal distributions versus generalized beta distribution in modeling the phenomenon of learning

Reference 18

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This paper cites The effect of environmental regulation on employment in resource-based areas of china—an empirical research based on the mediating effect model.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants The effect of environmental regulation on employment in resource-based areas of china—an empirical research based on the mediating effect model

Reference 19

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This paper cites Black Power, volume 48.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Black Power, volume 48

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This paper cites Fairness in Machine Learning: A Survey.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Fairness in Machine Learning: A Survey

Reference 21

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants American Community Survey Design and Methodology

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants American Community Survey Design and Methodology

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants American Community Survey and Puerto Rico Community Survey Design and Methodology

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants 2023 ACS 1-Year PUMS Data Dictionary

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This paper cites From race-based to race-conscious medicine: How anti-racist uprisings call us to act.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants From race-based to race-conscious medicine: How anti-racist uprisings call us to act

Reference 26

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This paper cites Changing opportunity: Sociological mechanisms underlying growing class gaps and shrinking race gaps in economic mobility.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Changing opportunity: Sociological mechanisms underlying growing class gaps and shrinking race gaps in economic mobility

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Path-specific counterfactual fairness

Reference 28

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Fair prediction with disparate impact: A study of bias in recidivism prediction instruments

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants A snapshot of the frontiers of fairness in machine learning

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Equality of educational opportunity

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Social capital in the creation of human capital

Reference 32

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants A spatial analysis of variations in health access: Linking geography, socio-economic status and access perceptions

Reference 33

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Poverty and education

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants The Measure and Mismeasure of Fairness

Reference 35

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Counterfactual risk assessments, evaluation, and fairness

Reference 36

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Mapping the margins: Intersectionality, identity politics, and violence against women of color

Reference 37

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Fairness is not static: Deeper understanding of long term fairness via simulation studies

Reference 38

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Critical Race Theory: An Introduction, volume 87

Reference 39

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Retiring adult: New datasets for fair machine learning

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Empirical risk minimization under fairness constraints

Reference 41

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Fairness through awareness

Reference 42

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Discrimination and Disrespect

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants A social vulnerability index for disaster management

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Incorporating area-level social drivers of health in predictive algorithms using electronic health record data

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants An intersectional definition of fairness

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Structural racism and health inequities: Old issues, new directions

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Central Problems in Social Theory: Action, Structure, and Contradiction in Social Analysis

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants What is Race? Four Philosophical Views

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Beyond distributive fairness in algorithmic decision making: Feature selection for procedurally fair learning

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Towards a critical race methodology in algorithmic fairness

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Equality of opportunity in supervised learning

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Gender and race: (what) are they? (what) do we want them to be? NO \^U S , 34 0 (1): 0 31--55, 2000

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Causal Inference: What If

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This paper cites Where fairness fails: Data, algorithms, and the limits of antidiscrimination discourse.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Where fairness fails: Data, algorithms, and the limits of antidiscrimination discourse

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Declining job quality in the united states: Explanations and evidence

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This paper cites What's sex got to do with machine learning? In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pages 513--513, 2020.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants What's sex got to do with machine learning? In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pages 513--513, 2020

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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Achieving long-term fairness in sequential decision making

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Observation 876f2a6d-02e5-4b1e-98c1-73eafa5345c2 · outbound

This paper cites Principal Fairness for Human and Algorithmic Decision-Making.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Principal Fairness for Human and Algorithmic Decision-Making

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-05T23:53:04.448540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 20d6f211-f284-4088-9008-26822135b927 · outbound

This paper cites Inequality: A reassessment of the effect of family and schooling in america, 1972.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Inequality: A reassessment of the effect of family and schooling in america, 1972

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:55.208430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b06c7297-fc2b-4e22-a3b2-47f50284c293 · outbound

This paper cites Addressing social vulnerability to hazards.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Addressing social vulnerability to hazards

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:55.379795Z

Source-reported events for the cited work

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Observation b6ca4fd5-a5b7-42a3-a35b-11c9b0d7c636 · outbound

This paper cites Quantifying explainable discrimination and removing illegal discrimination in automated decision making.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Quantifying explainable discrimination and removing illegal discrimination in automated decision making

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:55.489142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a58b6acd-4586-4c98-9c77-08f5897cc13a · outbound

This paper cites Algorithmic fairness and structural injustice: Insights from feminist political philosophy.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Algorithmic fairness and structural injustice: Insights from feminist political philosophy

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:55.624496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df6ad956-ace0-41d2-8707-ec7e9d08ceb2 · outbound

This paper cites The use and misuse of counterfactuals in ethical machine learning.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants The use and misuse of counterfactuals in ethical machine learning

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:55.808927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 075ad068-5e17-494b-b19a-07b935b8c343 · outbound

This paper cites The Ethical Algorithm: The Science of Socially Aware Algorithm Design.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants The Ethical Algorithm: The Science of Socially Aware Algorithm Design

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:55.952055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51b9f932-7e36-4593-afde-e520e17d8b77 · outbound

This paper cites Preventing fairness gerrymandering: Auditing and learning for subgroup fairness.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Preventing fairness gerrymandering: Auditing and learning for subgroup fairness

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:56.104589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1d21803-61d0-46f8-97ef-fe2543073516 · outbound

This paper cites Avoiding discrimination through causal reasoning.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Avoiding discrimination through causal reasoning

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:56.260641Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7ed25d8-28e5-4f2c-939d-e97325a07552 · outbound

This paper cites Making neighborhood-disadvantage metrics accessible--the neighborhood atlas.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Making neighborhood-disadvantage metrics accessible--the neighborhood atlas

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:56.339568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56874111-3403-4ebb-898e-2d99a4fb571b · outbound

This paper cites Neighborhood socioeconomic disadvantage and 30-day rehospitalization: A retrospective cohort study.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Neighborhood socioeconomic disadvantage and 30-day rehospitalization: A retrospective cohort study

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:56.473496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23f07a92-df11-456f-9ec4-90a119341a14 · outbound

This paper cites intersectionally fair.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants intersectionally fair

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:56.574127Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdcd6c0b-0818-4ad8-9bee-4b1ea068ac75 · outbound

This paper cites Predicting who reoffends: The neglected role of neighborhood context in recidivism studies.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Predicting who reoffends: The neglected role of neighborhood context in recidivism studies

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:56.676883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9410eb25-91d1-4683-a5d7-2beb9fa34a2d · outbound

This paper cites Counterfactual fairness.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Counterfactual fairness

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:56.774959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64823929-4cad-4eb2-a95a-83401ab31bd3 · outbound

This paper cites The badness of discrimination.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants The badness of discrimination

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:12.081329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0446086c-e9b0-40a8-970b-d040e0136ef0 · outbound

This paper cites Delayed impact of fair machine learning.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Delayed impact of fair machine learning

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:12.067415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9c698361-d306-43f1-bb53-f4270053b0d7 · outbound

This paper cites Causal Reasoning for Algorithmic Fairness.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Causal Reasoning for Algorithmic Fairness

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:57.089236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e752d4d9-a782-4633-b7c9-82354531a4f7 · outbound

This paper cites Neighborhoods, obesity, and diabetes--a randomized social experiment.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Neighborhoods, obesity, and diabetes--a randomized social experiment

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:12.052823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 85316e35-79e1-4498-a326-a061e762cb9e · outbound

This paper cites Survey on Causal-based Machine Learning Fairness Notions.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Survey on Causal-based Machine Learning Fairness Notions

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:57.194673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8104cec1-1012-481b-b4b8-1f220261b72b · outbound

This paper cites Environmental and health impacts of air pollution: A review.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Environmental and health impacts of air pollution: A review

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:12.038127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8e575b13-b325-4d82-ba37-b7e81ef0041d · outbound

This paper cites Social Determinants of Health.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Social Determinants of Health

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:12.023336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f30a8b85-061b-4691-95fc-3ec80487e28c · outbound

This paper cites Fairness-aware learning for continuous attributes and treatments.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Fairness-aware learning for continuous attributes and treatments

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:12.009379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d66dbc3b-3fa8-450d-a4d1-51c23c74d259 · outbound

This paper cites The prodigal paradigm returns: ecology comes back to sociology.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants The prodigal paradigm returns: ecology comes back to sociology

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.995472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8675b8d1-f5f0-4431-bffb-97f1ca988083 · outbound

This paper cites A survey on bias and fairness in machine learning.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants A survey on bias and fairness in machine learning

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.981269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e93d623a-554e-4802-bf18-7720cc83ecbe · outbound

This paper cites Fairness in risk assessment instruments: Post-processing to achieve counterfactual equalized odds.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Fairness in risk assessment instruments: Post-processing to achieve counterfactual equalized odds

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.967552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2c79dba9-d0f0-432e-affc-103c0de127ee · outbound

This paper cites Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Prediction-Based Decisions and Fairness: A Catalogue of Choices, Assumptions, and Definitions

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:57.733679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 833c5d09-f8db-4d74-b86d-83c0d199bea3 · outbound

This paper cites Equality and discrimination.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Equality and discrimination

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.952634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4d0ce895-2c37-42e6-a0af-3c3754109e87 · outbound

This paper cites Fair inference on outcomes.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Fair inference on outcomes

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.938374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation df9147cb-cfc5-4eb9-902c-63c178f7eea8 · outbound

This paper cites Learning optimal fair policies.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Learning optimal fair policies

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.924635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 92e94489-361e-4b4b-9e21-5cef5c8ce558 · outbound

This paper cites Optimal training of fair predictive models.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Optimal training of fair predictive models

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.910470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fc27f726-ccdc-4a80-a652-41ba0c8b37d2 · outbound

This paper cites Translation tutorial: 21 fairness definitions and their politics.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Translation tutorial: 21 fairness definitions and their politics

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.895804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 797af294-d12f-433f-a689-595171e9dfa7 · outbound

This paper cites Causal conceptions of fairness and their consequences.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Causal conceptions of fairness and their consequences

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.879824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ad80f90d-ac17-4b02-90e2-8585ecfafefc · outbound

This paper cites Dissecting racial bias in an algorithm used to manage the health of populations.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Dissecting racial bias in an algorithm used to manage the health of populations

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.865719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c0c7c8d7-a204-4d35-a66a-df925c19e13c · outbound

This paper cites Structural racism: A 60-year-old black woman with breast cancer.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Structural racism: A 60-year-old black woman with breast cancer

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.851632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f0262a5b-f126-43f3-8d31-64d6a661f374 · outbound

This paper cites Causality.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Causality

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.837616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 59cbd7c4-96ad-41e7-ae2b-607e0ec172b4 · outbound

This paper cites A review on fairness in machine learning.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants A review on fairness in machine learning

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.823811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 51875cfe-a80d-460d-9706-50730bad6d78 · outbound

This paper cites Elements of Causal Inference: Foundations and Learning Algorithms.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Elements of Causal Inference: Foundations and Learning Algorithms

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-05T23:52:58.610079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 323a021f-c8d7-4435-bc02-de638bc5d5b3 · outbound

This paper cites Legislating against discrimination: An international survey of anti-discrimination norms.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Legislating against discrimination: An international survey of anti-discrimination norms

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.799849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 71964918-c54e-45b2-b69b-1a23f694ddab · outbound

This paper cites Structural Injustice: Power, Advantage, and Human Rights.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Structural Injustice: Power, Advantage, and Human Rights

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.785815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 30db1368-65f6-40d7-b613-2fc23adf6249 · outbound

This paper cites Environmental regulation and employment in resource-based cities in china: The threshold effect of industrial structure transformation.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Environmental regulation and employment in resource-based cities in china: The threshold effect of industrial structure transformation

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.771907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c211e41c-e775-453e-9357-aedad66644ab · outbound

This paper cites A Theory of Justice.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants A Theory of Justice

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.758091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 43c56d55-6690-4d53-aceb-5fdd8f1b56ce · outbound

This paper cites Justice as Fairness: A Restatement.

Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants Justice as Fairness: A Restatement

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T23:53:11.744583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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Pith citing papers