mlsanityData to 14 Oct 2026

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

arXiv 2604.26851 ↗First posted 29 Apr 2026cs.CY cs.AIcited by 1

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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.

Key facts. Cited by 1 paper 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

23 of 78 references.

References · 23

  1. 2018Confounding variables can degrade generalization performance of radiological deep learning modelscited by 379
  2. 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 174
  3. 2020Learning to Complement Humanscited by 124
  4. 2021To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-makingcited by 118
  5. 2022Explanations Can Reduce Overreliance on AI Systems During Decision-Makingcited by 111
  6. 2024Sycophancy in Large Language Models: Causes and Mitigationscited by 101
  7. 2021Towards a Science of Human-AI Decision Making: A Survey of Empirical Studiescited by 86
  8. 2020A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scorescited by 74
  9. 2023Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystemcited by 52
  10. 2024Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrievalcited by 51
  11. 2019What does it mean to solve the problem of discrimination in hiring? Social, technical and legal perspectives from the UK on automated hiring systemscited by 38
  12. 2024The Silicon Ceiling: Auditing GPT's Race and Gender Biases in Hiringcited by 29
11 more
  1. 2024Identifying and Improving Disability Bias in GPT-Based Resume Screeningcited by 28
  2. 2020Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contextscited by 22
  3. 2024The Impossibility of Fair LLMscited by 22
  4. 2021A Silicon Valley Love Triangle: Hiring Algorithms, Pseudo-Science, and the Quest for Auditabilitycited by 16
  5. 2024Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Healthcare Professionalscited by 13
  6. 2023Algorithmic Harms in Child Welfare: Uncertainties in Practice, Organization, and Street-level Decision-Makingcited by 12
  7. 2025Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literaturecited by 11
  8. 2025No Thoughts Just AI: Biased LLM Hiring Recommendations Alter Human Decision Making and Limit Human Autonomycited by 8
  9. 2024Towards Effective Discrimination Testing for Generative AIcited by 5
  10. 2023SoK: Safer Digital-Safety Research Involving At-Risk Userscited by 3
  11. 2025An External Fairness Evaluation of LinkedIn Talent Searchcited by 3

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