# Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making

arXiv: https://arxiv.org/abs/1806.04959 · HTML: https://mlsanity.com/paper/1806.04959/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 30 of 46)

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- [Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Research](https://mlsanity.com/paper/2102.01203/) (2021)
- [Two-sided fairness in rankings via Lorenz dominance](https://mlsanity.com/paper/2110.15781/) (2021)
- [Training Unbiased Diffusion Models From Biased Dataset](https://mlsanity.com/paper/2403.01189/) (2024)
- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning](https://mlsanity.com/paper/2104.14504/) (2021)
- [What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective](https://mlsanity.com/paper/2206.04101/) (2022)
- [Optimizing generalized Gini indices for fairness in rankings](https://mlsanity.com/paper/2204.06521/) (2022)
- [Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing](https://mlsanity.com/paper/2206.11423/) (2022)
- [An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settings](https://mlsanity.com/paper/2105.06442/) (2021)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [Roadmap on Incentive Compatibility for AI Alignment and Governance in Sociotechnical Systems](https://mlsanity.com/paper/2402.12907/) (2024)
- [Disaggregated Interventions to Reduce Inequality](https://mlsanity.com/paper/2107.00593/) (2021)
- [Counterfactuals for the Future](https://mlsanity.com/paper/2212.03974/) (2022)
- [Regulatory Instruments for Fair Personalized Pricing](https://mlsanity.com/paper/2202.04245/) (2022)
- [A Theoretical Approach to Characterize the Accuracy-Fairness Trade-off Pareto Frontier](https://mlsanity.com/paper/2310.12785/) (2023)
- [Fairness through Social Welfare Optimization](https://mlsanity.com/paper/2102.00311/) (2021)
- [Rawlsian Fair Adaptation of Deep Learning Classifiers](https://mlsanity.com/paper/2105.14890/) (2021)
- [A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs](https://mlsanity.com/paper/2206.02891/) (2022)
- [Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics](https://mlsanity.com/paper/2206.02897/) (2022)
- [It's Not Fairness, and It's Not Fair: The Failure of Distributional Equality and the Promise of Relational Equality in Complete-Information Hiring Games](https://mlsanity.com/paper/2209.05602/) (2022)
- [Procedural Fairness Through Decoupling Objectionable Data Generating Components](https://mlsanity.com/paper/2311.14688/) (2023)
- [Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmap](https://mlsanity.com/paper/2502.11981/) (2025)
- [Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2606.18111/) (2026)
- [Explainability-aided Domain Generalization for Image Classification](https://mlsanity.com/paper/2104.01742/) (2021)
- [Fairness Through Counterfactual Utilities](https://mlsanity.com/paper/2108.05315/) (2021)
- [Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty](https://mlsanity.com/paper/2205.06483/) (2022)
- [Fair Clustering: Critique, Caveats, and Future Directions](https://mlsanity.com/paper/2406.15960/) (2024)

## Abstract (arXiv)

We draw attention to an important, yet largely overlooked aspect of evaluating fairness for automated decision making systems---namely risk and welfare considerations. Our proposed family of measures corresponds to the long-established formulations of cardinal social welfare in economics, and is justified by the Rawlsian conception of fairness behind a veil of ignorance. The convex formulation of our welfare-based measures of fairness allows us to integrate them as a constraint into any convex loss minimization pipeline. Our empirical analysis reveals interesting trade-offs between our proposal and (a) prediction accuracy, (b) group discrimination, and (c) Dwork et al.'s notion of individual fairness. Furthermore and perhaps most importantly, our work provides both heuristic justification and empirical evidence suggesting that a lower-bound on our measures often leads to bounded inequality in algorithmic outcomes; hence presenting the first computationally feasible mechanism for bounding individual-level inequality.