mlsanityData to 14 Oct 2026

Fair Sequential Selection Using Supervised Learning Models

arXiv 2110.13986 ↗First posted 26 Oct 2021cs.LG cs.AI cs.CYcited by 16

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Abstract (arXiv)

We consider a selection problem where sequentially arrived applicants apply for a limited number of positions/jobs. At each time step, a decision maker accepts or rejects the given applicant using a pre-trained supervised learning model until all the vacant positions are filled. In this paper, we discuss whether the fairness notions (e.g., equal opportunity, statistical parity, etc.) that are commonly used in classification problems are suitable for the sequential selection problems. In particular, we show that even with a pre-trained model that satisfies the common fairness notions, the selection outcomes may still be biased against certain demographic groups. This observation implies that the fairness notions used in classification problems are not suitable for a selection problem where the applicants compete for a limited number of positions. We introduce a new fairness notion, ``Equal Selection (ES),'' suitable for sequential selection problems and propose a post-processing approach to satisfy the ES fairness notion. We also consider a setting where the applicants have privacy concerns, and the decision maker only has access to the noisy version of sensitive attributes. In this setting, we can show that the perfect ES fairness can still be attained under certain conditions.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 25 of its 43 references that are in the map. First posted on arXiv 26 Oct 2021.

Builds on

25 of 43 references.

References · 25

  1. 2016Deep Learning with Differential Privacycited by 2,728
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  3. 2018A Tutorial on Bayesian Optimizationcited by 744
  4. 2018A Reductions Approach to Fair Classificationcited by 587
  5. 2017On Fairness and Calibrationcited by 406
  6. 2017Algorithmic decision making and the cost of fairnesscited by 403
  7. 2016Fairness in Learning: Classic and Contextual Banditscited by 168
  8. 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
  9. 2016Fairness in Reinforcement Learningcited by 95
  10. 2019Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combinationcited by 79
  11. 2020Robust Optimization for Fairness with Noisy Protected Groupscited by 78
  12. 2018Differentially Private Fair Learningcited by 76
13 more
  1. 2020How Do Fair Decisions Fare in Long-term Qualification?cited by 60
  2. 2019Equalized odds postprocessing under imperfect group informationcited by 48
  3. 2020Fair Learning with Private Demographic Datacited by 46
  4. 2019Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairnesscited by 38
  5. 2020Fairness in Learning-Based Sequential Decision Algorithms: A Surveycited by 35
  6. 2018Individual Fairness in Hindsightcited by 33
  7. 2018Selection Problems in the Presence of Implicit Biascited by 29
  8. 2020Individual Fairness in Pipelinescited by 22
  9. 2018Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithmscited by 21
  10. 2019An Algorithmic Framework for Fairness Elicitationcited by 16
  11. 2019Efficient candidate screening under multiple tests and implications for fairnesscited by 16
  12. 2020Improving Fairness and Privacy in Selection Problemscited by 16
  13. 2019Local Differential Privacy: a tutorialcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 3; 2025: 5; 2026: 1.

16 papers.

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  1. 2022Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Makingcited by 13
  2. 2023Loss Balancing for Fair Supervised Learningcited by 11
  3. 2023Counterfactually Fair Representationcited by 9
  4. 2022Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipelinecited by 8
  5. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  6. 2024Classification Under Strategic Self-Selectioncited by 7
  7. 2024Lookahead Counterfactual Fairnesscited by 2
  8. 2025Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifierscited by 2
  9. 2023Federated Learning with Reduced Information Leakage and Computationcited by 1
  10. 2023Learning Fair Policies for Multi-stage Selection Problems from Observational Datacited by 1
  11. 2024Approximating Discrimination Within Models When Faced With Several Non-Binary Sensitive Attributescited by 1
  12. 2025Algorithmic Hiring and Diversity: Reducing Human-Algorithm Similarity for Better Outcomescited by 1
4 more
  1. 2025Demographic-Agnostic Fairness without Harmcited by 1
  2. 2025Achieving Fairness Without Harm via Selective Demographic Expertscited by 1
  3. 2026Benchmarking Bias Mitigation Toward Fairness Without Harm from Vision to LVLMscited by 1
  4. 2025On Mitigating Affinity Bias through Bandits with Evolving Biased Feedbackcited by 0