# Fair Sequential Selection Using Supervised Learning Models

arXiv: https://arxiv.org/abs/2110.13986 · HTML: https://mlsanity.com/paper/2110.13986/

## 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 (most cited first, 25 of 25)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Equity of Attention: Amortizing Individual Fairness in Rankings](https://mlsanity.com/paper/1805.01788/) (2018)
- [Fairness in Reinforcement Learning](https://mlsanity.com/paper/1611.03071/) (2016)
- [Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination](https://mlsanity.com/paper/1906.00285/) (2019)
- [Robust Optimization for Fairness with Noisy Protected Groups](https://mlsanity.com/paper/2002.09343/) (2020)
- [Differentially Private Fair Learning](https://mlsanity.com/paper/1812.02696/) (2018)
- [How Do Fair Decisions Fare in Long-term Qualification?](https://mlsanity.com/paper/2010.11300/) (2020)
- [Equalized odds postprocessing under imperfect group information](https://mlsanity.com/paper/1906.03284/) (2019)
- [Fair Learning with Private Demographic Data](https://mlsanity.com/paper/2002.11651/) (2020)
- [Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness](https://mlsanity.com/paper/1905.00569/) (2019)
- [Fairness in Learning-Based Sequential Decision Algorithms: A Survey](https://mlsanity.com/paper/2001.04861/) (2020)
- [Individual Fairness in Hindsight](https://mlsanity.com/paper/1812.04069/) (2018)
- [Selection Problems in the Presence of Implicit Bias](https://mlsanity.com/paper/1801.03533/) (2018)
- [Individual Fairness in Pipelines](https://mlsanity.com/paper/2004.05167/) (2020)
- [Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms](https://mlsanity.com/paper/1806.02246/) (2018)
- [An Algorithmic Framework for Fairness Elicitation](https://mlsanity.com/paper/1905.10660/) (2019)
- [Efficient candidate screening under multiple tests and implications for fairness](https://mlsanity.com/paper/1905.11361/) (2019)
- [Improving Fairness and Privacy in Selection Problems](https://mlsanity.com/paper/2012.03812/) (2020)
- [Local Differential Privacy: a tutorial](https://mlsanity.com/paper/1907.11908/) (2019)

## Built on by (most cited first, 16 of 16)

- [Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making](https://mlsanity.com/paper/2205.04790/) (2022)
- [Loss Balancing for Fair Supervised Learning](https://mlsanity.com/paper/2311.03714/) (2023)
- [Counterfactually Fair Representation](https://mlsanity.com/paper/2311.05420/) (2023)
- [Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline](https://mlsanity.com/paper/2203.07513/) (2022)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [Classification Under Strategic Self-Selection](https://mlsanity.com/paper/2402.15274/) (2024)
- [Lookahead Counterfactual Fairness](https://mlsanity.com/paper/2412.01065/) (2024)
- [Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers](https://mlsanity.com/paper/2503.06150/) (2025)
- [Federated Learning with Reduced Information Leakage and Computation](https://mlsanity.com/paper/2310.06341/) (2023)
- [Learning Fair Policies for Multi-stage Selection Problems from Observational Data](https://mlsanity.com/paper/2312.13173/) (2023)
- [Approximating Discrimination Within Models When Faced With Several Non-Binary Sensitive Attributes](https://mlsanity.com/paper/2408.06099/) (2024)
- [Algorithmic Hiring and Diversity: Reducing Human-Algorithm Similarity for Better Outcomes](https://mlsanity.com/paper/2505.14388/) (2025)
- [Demographic-Agnostic Fairness without Harm](https://mlsanity.com/paper/2509.24077/) (2025)
- [Achieving Fairness Without Harm via Selective Demographic Experts](https://mlsanity.com/paper/2511.06293/) (2025)
- [Benchmarking Bias Mitigation Toward Fairness Without Harm from Vision to LVLMs](https://mlsanity.com/paper/2602.03895/) (2026)
- [On Mitigating Affinity Bias through Bandits with Evolving Biased Feedback](https://mlsanity.com/paper/2503.05662/) (2025)

## 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.