# Resume-ing Control: (Mis)Perceptions of Agency Around GenAI Use in Recruiting Workflows

arXiv: https://arxiv.org/abs/2604.26851 · HTML: https://mlsanity.com/paper/2604.26851/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 78 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 23 of 23)

- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [Learning to Complement Humans](https://mlsanity.com/paper/2005.00582/) (2020)
- [To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making](https://mlsanity.com/paper/2102.09692/) (2021)
- [Explanations Can Reduce Overreliance on AI Systems During Decision-Making](https://mlsanity.com/paper/2212.06823/) (2022)
- [Sycophancy in Large Language Models: Causes and Mitigations](https://mlsanity.com/paper/2411.15287/) (2024)
- [Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies](https://mlsanity.com/paper/2112.11471/) (2021)
- [A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores](https://mlsanity.com/paper/2002.08035/) (2020)
- [Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem](https://mlsanity.com/paper/2310.02521/) (2023)
- [Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval](https://mlsanity.com/paper/2407.20371/) (2024)
- [What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systems](https://mlsanity.com/paper/1910.06144/) (2019)
- [The Silicon Ceiling: Auditing GPT's Race and Gender Biases in Hiring](https://mlsanity.com/paper/2405.04412/) (2024)
- [Identifying and Improving Disability Bias in GPT-Based Resume Screening](https://mlsanity.com/paper/2402.01732/) (2024)
- [Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contexts](https://mlsanity.com/paper/2012.05370/) (2020)
- [The Impossibility of Fair LLMs](https://mlsanity.com/paper/2406.03198/) (2024)
- [A Silicon Valley Love Triangle: Hiring Algorithms, Pseudo-Science, and the Quest for Auditability](https://mlsanity.com/paper/2106.12403/) (2021)
- [Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Healthcare Professionals](https://mlsanity.com/paper/2403.14691/) (2024)
- [Algorithmic Harms in Child Welfare: Uncertainties in Practice, Organization, and Street-level Decision-Making](https://mlsanity.com/paper/2308.05224/) (2023)
- [Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature](https://mlsanity.com/paper/2507.06000/) (2025)
- [No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomy](https://mlsanity.com/paper/2509.04404/) (2025)
- [Towards Effective Discrimination Testing for Generative AI](https://mlsanity.com/paper/2412.21052/) (2024)
- [SoK: Safer Digital-Safety Research Involving At-Risk Users](https://mlsanity.com/paper/2309.00735/) (2023)
- [An External Fairness Evaluation of LinkedIn Talent Search](https://mlsanity.com/paper/2511.10752/) (2025)

## Built on by (most cited first, 1 of 1)

- [An Empirical Study and Assessment of EU AI Act Compliance Checkers](https://mlsanity.com/paper/2609.36228/) (2026)

## Abstract (arXiv)

When generative AI (genAI) systems are used in high-stakes decision-making, its recommended role is to aid, rather than replace, human decision-making. However, there is little empirical exploration of how professionals making high-stakes decisions, such as those related to employment, perceive their agency and level of control when working with genAI systems. Through interviews with 22 recruiting professionals, we investigate how genAI subtly influences control over everyday workflows and even individual hiring decisions. Our findings highlight a pressing conflict: while recruiters believe they have final authority across the recruiting pipeline, genAI has become an invisible architect that shapes the foundational building blocks of information used for evaluation, from defining a job to determining good interview performances. The decision of whether or not to adopt was also often outside recruiters' control, with many feeling compelled to adopt genAI due to calls to integrate AI from higher-ups in their business, to combat applicant use of AI, and the individual need to boost productivity. Despite a seemingly seismic shift in how recruiting happens, participants only reported marginal efficiency gains. Such gains came at the high cost of recruiter deskilling, a trend that jeopardizes the meaningful oversight of decision-making. We conclude by discussing the implications of such findings for responsible and perceptible genAI use in hiring contexts.