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arxiv: 2604.26851 · v1 · submitted 2026-04-29 · 💻 cs.CY · cs.AI

Recognition: unknown

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

Sajel Surati , Rosanna Bellini , Emily Black
Authors on Pith no claims yet

Pith reviewed 2026-05-07 11:00 UTC · model grok-4.3

classification 💻 cs.CY cs.AI
keywords generative AIrecruiting workflowsperceptions of agencyhiring decisionsdeskillinginvisible influencehuman oversighthigh-stakes decisions
0
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The pith

GenAI shapes the core information recruiters use to define jobs and judge candidates, even as they believe they retain final authority over hiring decisions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how recruiting professionals experience agency and control when generative AI enters their workflows for high-stakes employment decisions. Interviews with 22 professionals show that recruiters view themselves as holding ultimate oversight across the pipeline, yet genAI quietly structures the underlying data from job descriptions through interview evaluations. Adoption often stems from external pressures such as directives from leadership or the need to match applicant AI use, yielding only modest efficiency improvements. These changes coincide with noticeable deskilling among recruiters, which undercuts effective human supervision. The findings matter because they question whether current practices preserve meaningful human judgment in hiring.

Core claim

Through interviews with 22 recruiting professionals, the authors find that recruiters believe they maintain final authority across the recruiting pipeline, yet 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 to adopt genAI is frequently outside recruiters' control, driven by calls from higher-ups, the need to counter applicant AI use, and productivity demands. Reported efficiency gains remain marginal and come at the cost of recruiter deskilling that jeopardizes meaningful oversight of decision-making.

What carries the argument

Interviews that expose misperceptions of agency, showing genAI influences decisions indirectly by redefining the information base rather than through direct overrides.

If this is right

  • Design of genAI tools for hiring must make their effects on foundational information visible to preserve perceived and actual control.
  • Deskilling among recruiters risks weakening the quality of human oversight in employment decisions over time.
  • Forced or pressured adoption of genAI can produce limited net benefits while eroding professional skills.
  • Perceptions of final authority do not align with the actual ways genAI structures evaluation inputs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same pattern of indirect shaping could affect other decision domains where AI preprocesses data before human review, such as loan approvals or medical triage.
  • Recruiter training could incorporate exercises that help users detect and adjust for AI-defined criteria in their workflows.
  • Larger-scale surveys or log-based studies of actual tool usage could test whether self-reported control matches observed changes in outputs.
  • Regulations on AI in hiring might need requirements for transparency about how AI contributes to job definitions and performance standards.

Load-bearing premise

Self-reported perceptions from interviews with 22 recruiting professionals accurately capture the real extent of genAI influence and extend beyond this specific group without major bias.

What would settle it

A controlled observation where recruiters' job postings, screening criteria, or final candidate selections change substantially once genAI-generated content is removed or its specific contributions are fully disclosed to them.

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper reports on semi-structured interviews with 22 recruiting professionals and claims that, despite recruiters' belief in retaining final authority over hiring decisions, generative AI functions as an 'invisible architect' that shapes foundational elements of the recruiting pipeline (job definitions, candidate evaluation criteria, and interview performance standards). It further argues that adoption decisions are often externally driven, efficiency gains are marginal, and the process risks recruiter deskilling, with implications for responsible GenAI deployment in employment contexts.

Significance. If the reported perceptions are robustly documented, the work would add timely empirical insight into human-AI agency dynamics in high-stakes professional settings, extending HCI and CSCW literature on subtle influence and deskilling. The qualitative focus on recruiting workflows offers concrete examples that could inform design guidelines for perceptible AI tools, though the absence of triangulation limits claims about actual (versus perceived) influence.

major comments (3)
  1. [Methods] Methods section: The manuscript supplies no information on sampling/recruitment procedures, interview protocol or guide, transcription process, thematic analysis steps (e.g., inductive vs. deductive coding), number of coders, or any trustworthiness measures such as inter-rater reliability or member checking. Because the central claims about misperceptions of agency and GenAI's 'invisible architect' role rest exclusively on this thematic analysis, the lack of methodological transparency is load-bearing for evaluating the evidence quality.
  2. [Abstract and Findings] Abstract and Findings: The assertion that 'genAI has become an invisible architect that shapes the foundational building blocks of information used for evaluation' is presented as an empirical finding rather than a participant-reported perception. The supporting data consist only of self-reported interview excerpts; no workflow observations, artifact comparisons (e.g., AI-generated vs. final job descriptions), adoption metrics, or organizational records are described, leaving the factual framing unsupported by the evidence provided.
  3. [Results and Discussion] Results/Discussion: Claims of 'marginal efficiency gains' and 'deskilling' are derived from participant self-reports without quantification, pre/post comparisons, or external validation. This weakens the ability to assess the magnitude or generalizability of the reported trade-offs, which are central to the paper's argument about jeopardized oversight.
minor comments (2)
  1. [Abstract] The abstract and introduction could more explicitly flag that all claims derive from perceptions rather than observed practices, to avoid conflating reported feelings with documented influence.
  2. [Methods or Results] Participant demographics (e.g., experience level, organization size, geographic distribution) are not summarized in a table or early section, making it harder to evaluate the sample's representativeness.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which has identified key areas where the manuscript can be strengthened in terms of transparency and precise framing. We address each major comment below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Methods] Methods section: The manuscript supplies no information on sampling/recruitment procedures, interview protocol or guide, transcription process, thematic analysis steps (e.g., inductive vs. deductive coding), number of coders, or any trustworthiness measures such as inter-rater reliability or member checking. Because the central claims about misperceptions of agency and GenAI's 'invisible architect' role rest exclusively on this thematic analysis, the lack of methodological transparency is load-bearing for evaluating the evidence quality.

    Authors: We agree that the Methods section requires substantial expansion for full transparency and replicability. The study used purposive sampling to recruit 22 recruiting professionals via LinkedIn outreach and professional associations in the recruiting field. Interviews followed a semi-structured guide addressing daily workflows, genAI tool adoption and use cases, perceptions of control and agency, efficiency impacts, and deskilling concerns. All sessions were audio-recorded with consent and transcribed verbatim. Analysis employed inductive thematic analysis per Braun and Clarke's six-phase framework, with two authors independently coding transcripts, comparing codes, and resolving discrepancies through discussion. Trustworthiness was supported via reflexive memos, team debriefing, and an audit trail of coding decisions. We will revise the Methods section to detail all of these elements and include the interview guide as supplementary material. revision: yes

  2. Referee: [Abstract and Findings] Abstract and Findings: The assertion that 'genAI has become an invisible architect that shapes the foundational building blocks of information used for evaluation' is presented as an empirical finding rather than a participant-reported perception. The supporting data consist only of self-reported interview excerpts; no workflow observations, artifact comparisons (e.g., AI-generated vs. final job descriptions), adoption metrics, or organizational records are described, leaving the factual framing unsupported by the evidence provided.

    Authors: We appreciate the referee's emphasis on accurate epistemological framing. Our work is a qualitative interview study centered on professionals' perceptions and experiences; we do not claim direct observation of genAI's architectural influence or possess artifact comparisons or metrics. We will revise the abstract and findings sections to explicitly qualify the claims as derived from participant accounts and thematic analysis (e.g., 'participants described genAI as functioning as an invisible architect...' and 'our analysis indicates that recruiters perceive...'). We will also strengthen the limitations discussion to note the absence of observational or artifact-based validation and the interpretive nature of the evidence. revision: yes

  3. Referee: [Results and Discussion] Results/Discussion: Claims of 'marginal efficiency gains' and 'deskilling' are derived from participant self-reports without quantification, pre/post comparisons, or external validation. This weakens the ability to assess the magnitude or generalizability of the reported trade-offs, which are central to the paper's argument about jeopardized oversight.

    Authors: We concur that reliance on self-reports without quantification or external benchmarks limits assessment of magnitude and generalizability. In revision we will add more precise reporting of theme prevalence (e.g., noting that the majority of participants described efficiency gains as marginal and providing additional representative excerpts). We will expand the limitations section to explicitly discuss the lack of pre/post data, behavioral measures, or organizational records and the consequent interpretive scope. While we cannot introduce new quantitative data at this stage, we believe the consistent patterns across the sample still offer valuable insight into perceived trade-offs and will frame the argument accordingly as perception-based. revision: partial

Circularity Check

0 steps flagged

No circularity in qualitative empirical study

full rationale

The paper is an empirical qualitative study relying on semi-structured interviews with 22 recruiting professionals followed by thematic analysis. No equations, derivations, fitted parameters, predictions, or mathematical models are present that could reduce to their own inputs. Central claims are framed as participant perceptions and themes extracted from interview data rather than self-derived results. Any self-citations (if present) are not load-bearing for uniqueness theorems or ansatzes; the evidence chain rests on external interview data and is self-contained against standard qualitative benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the assumption that interview self-reports validly capture both perceptions and the actual shaping role of GenAI, plus that the small convenience sample supports broader conclusions about recruiting workflows.

axioms (1)
  • domain assumption Self-reported perceptions from interviews accurately reflect participants' actual experiences and the influence of GenAI on their workflows.
    Qualitative interview studies require this assumption to treat reported beliefs as evidence of real control dynamics.

pith-pipeline@v0.9.0 · 5535 in / 1145 out tokens · 68715 ms · 2026-05-07T11:00:22.206344+00:00 · methodology

discussion (0)

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