# The Initial Screening Order Problem

arXiv: https://arxiv.org/abs/2307.15398 · HTML: https://mlsanity.com/paper/2307.15398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 84 references that are in the map.
- First posted on arXiv 28 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [FA*IR: A Fair Top-k Ranking Algorithm](https://mlsanity.com/paper/1706.06368/) (2017)
- [Measuring Fairness in Ranked Outputs](https://mlsanity.com/paper/1610.08559/) (2016)
- [Fairness in Rankings and Recommendations: An Overview](https://mlsanity.com/paper/2104.05994/) (2021)
- [Selection Problems in the Presence of Implicit Bias](https://mlsanity.com/paper/1801.03533/) (2018)
- [Mitigating Bias in Set Selection with Noisy Protected Attributes](https://mlsanity.com/paper/2011.04219/) (2020)
- [Fairness in Information Access Systems](https://mlsanity.com/paper/2105.05779/) (2021)
- [Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context](https://mlsanity.com/paper/2006.09663/) (2020)
- [A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics](https://mlsanity.com/paper/2307.03195/) (2023)
- [A Silicon Valley Love Triangle: Hiring Algorithms, Pseudo-Science, and the Quest for Auditability](https://mlsanity.com/paper/2106.12403/) (2021)
- [An External Stability Audit Framework to Test the Validity of Personality Prediction in AI Hiring](https://mlsanity.com/paper/2201.09151/) (2022)
- [Fairness in Agreement With European Values: An Interdisciplinary Perspective on AI Regulation](https://mlsanity.com/paper/2207.01510/) (2022)
- [An Agent-based Model to Evaluate Interventions on Online Dating Platforms to Decrease Racial Homogamy](https://mlsanity.com/paper/2103.03332/) (2021)
- [On Meritocracy in Optimal Set Selection](https://mlsanity.com/paper/2102.11932/) (2021)

## Abstract (arXiv)

We investigate the role of the initial screening order (ISO) in candidate screening. The ISO refers to the order in which the screener searches the candidate pool when selecting k candidates. Today, it is common for the ISO to be the product of an information access system, such as an online platform or a database query. The ISO has been largely overlooked in the literature, despite its impact on the optimality and fairness of the selected k candidates, especially under a human screener. We define two problem formulations describing the search behavior of the screener given an ISO: the best-k, where it selects the top k candidates; and the good-k, where it selects the first good-enough k candidates. To study the impact of the ISO, we introduce a human-like screener and compare it to its algorithmic counterpart, where the human-like screener is conceived to be inconsistent over time. Our analysis, in particular, shows that the ISO, under a human-like screener solving for the good-k problem, hinders individual fairness despite meeting group fairness, and hampers the optimality of the selected k candidates. This is due to position bias, where a candidate's evaluation is affected by its position within the ISO. We report extensive simulated experiments exploring the parameters of the best-k and good-k problems for both screeners. Our simulation framework is flexible enough to account for multiple candidate screening tasks, being an alternative to running real-world procedures.