mlsanityData to 14 Oct 2026

Disentangling and Operationalizing AI Fairness at LinkedIn

arXiv 2306.00025 ↗First posted 30 May 2023cs.SI cs.AIcited by 7

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Abstract (arXiv)

Operationalizing AI fairness at LinkedIn's scale is challenging not only because there are multiple mutually incompatible definitions of fairness but also because determining what is fair depends on the specifics and context of the product where AI is deployed. Moreover, AI practitioners need clarity on what fairness expectations need to be addressed at the AI level. In this paper, we present the evolving AI fairness framework used at LinkedIn to address these three challenges. The framework disentangles AI fairness by separating out equal treatment and equitable product expectations. Rather than imposing a trade-off between these two commonly opposing interpretations of fairness, the framework provides clear guidelines for operationalizing equal AI treatment complemented with a product equity strategy. This paper focuses on the equal AI treatment component of LinkedIn's AI fairness framework, shares the principles that support it, and illustrates their application through a case study. We hope this paper will encourage other big tech companies to join us in sharing their approach to operationalizing AI fairness at scale, so that together we can keep advancing this constantly evolving field.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 38 of its 84 references that are in the map. First posted on arXiv 30 May 2023.

Builds on

38 of 84 references.

References · 38

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  2. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  3. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  4. 2017On Fairness and Calibrationcited by 406
  5. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 364
  6. 2022Prompting GPT-3 To Be Reliablecited by 260
  7. 2020Fairness without Demographics through Adversarially Reweighted Learningcited by 251
  8. 2018Multiaccuracy: Black-Box Post-Processing for Fairness in Classificationcited by 222
  9. 2020Fairness in Machine Learningcited by 194
  10. 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 174
  11. 2016Fairness in Learning: Classic and Contextual Banditscited by 168
  12. 2018Counterfactual Fairness in Text Classification through Robustnesscited by 151
26 more
  1. 2016Fairness in Reinforcement Learningcited by 95
  2. 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
  3. 2018Privacy Preserving Machine Learning: Threats and Solutionscited by 79
  4. 2018Bias Mitigation Post-processing for Individual and Group Fairnesscited by 72
  5. 2019Putting Fairness Principles into Practice: Challenges, Metrics, and Improvementscited by 70
  6. 2021Post-processing for Individual Fairnesscited by 61
  7. 2020"What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairnesscited by 56
  8. 2022Differential Privacy and Fairness in Decisions and Learning Tasks: A Surveycited by 47
  9. 2020Improving the Fairness of Deep Generative Models without Retrainingcited by 46
  10. 2019AdaFair: Cumulative Fairness Adaptive Boostingcited by 45
  11. 2021Fairness for Image Generation with Uncertain Sensitive Attributescited by 31
  12. 2020LiFT: A Scalable Framework for Measuring Fairness in ML Applicationscited by 22
  13. 2021Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systemscited by 21
  14. 2018Towards Deep and Representation Learning for Talent Search at LinkedIncited by 19
  15. 2019On the Legal Compatibility of Fairness Definitionscited by 19
  16. 2021Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AIcited by 16
  17. 2022Pushing the limits of fairness impossibility: Who's the fairest of them all?cited by 15
  18. 2021FairCanary: Rapid Continuous Explainable Fairnesscited by 12
  19. 2018Talent Search and Recommendation Systems at LinkedIn: Practical Challenges and Lessons Learnedcited by 11
  20. 2021A/B Testing for Recommender Systems in a Two-sided Marketplacecited by 11
  21. 2018Inference for Individual Mediation Effects and Interventional Effects in Sparse High-Dimensional Causal Graphical Modelscited by 7
  22. 2020Fairness through Experimentation: Inequality in A/B testing as an approach to responsible designcited by 4
  23. 2021An Outcome Test of Discrimination for Ranked Listscited by 4
  24. 2018Explore-Exploit: A Framework for Interactive and Online Learningcited by 2
  25. 2019A/B Testing in Dense Large-Scale Networks: Design and Inferencecited by 1
  26. 2023An Operational Perspective to Fairness Interventions: Where and How to Intervenecited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 1; 2026: 2.

7 papers.

  1. 2024Natural Language Processing for Human Resources: A Surveycited by 10
  2. 2023Kantian Deontology Meets AI Alignment: Towards Morally Grounded Fairness Metricscited by 1
  3. 2024INFELM: In-depth Fairness Evaluation of Large Text-To-Image Modelscited by 1
  4. 2024Privacy-Preserving Race/Ethnicity Estimation for Algorithmic Bias Measurement in the U.Scited by 0
  5. 2025Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systemscited by 0
  6. 2026Data Bias Mitigation under Coverage Constraints & The Price of Fairnesscited by 0
  7. 2026What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Researchcited by 0