mlsanityData to 7 Oct 2026

RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity

arXiv 2104.03909 ↗First posted 16 Mar 2021cs.CY cs.AI cs.LGcited by 4

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Abstract (arXiv)

We present RAWLSNET, a system for altering Bayesian Network (BN) models to satisfy the Rawlsian principle of fair equality of opportunity (FEO). RAWLSNET's BN models generate aspirational data distributions: data generated to reflect an ideally fair, FEO-satisfying society. FEO states that everyone with the same talent and willingness to use it should have the same chance of achieving advantageous social positions (e.g., employment), regardless of their background circumstances (e.g., socioeconomic status). Satisfying FEO requires alterations to social structures such as school assignments. Our paper describes RAWLSNET, a method which takes as input a BN representation of an FEO application and alters the BN's parameters so as to satisfy FEO when possible, and minimize deviation from FEO otherwise. We also offer guidance for applying RAWLSNET, including on recognizing proper applications of FEO. We demonstrate the use of our system with publicly available data sets. RAWLSNET's altered BNs offer the novel capability of generating aspirational data for FEO-relevant tasks. Aspirational data are free from the biases of real-world data, and thus are useful for recognizing and detecting sources of unfairness in machine learning algorithms besides biased data.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 54 references that are in the map. First posted on arXiv 16 Mar 2021.

Builds on

11 of 54 references.

References · 11

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  4. 2016Fairness in Learning: Classic and Contextual Banditscited by 167
  5. 2017Data Decisions and Theoretical Implications when Adversarially Learning Fair Representationscited by 165
  6. 2020Algorithmic Fairnesscited by 143
  7. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  8. 2018Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobservedcited by 94
  9. 2018A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunitycited by 53
  10. 2019Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patternscited by 14
  11. 2020Measuring justice in machine learningcited by 7

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Cited by year

Citing papers per year: 2022: 2; 2023: 2.

4 papers.

  1. 2022Towards Substantive Conceptions of Algorithmic Fairness: Normative Guidance from Equal Opportunity Doctrinescited by 10
  2. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  3. 2022The Equity Framework: Fairness Beyond Equalized Predictive Outcomescited by 2
  4. 2023From Utilitarian to Rawlsian Designs for Algorithmic Fairnesscited by 0