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arxiv: 2606.22213 · v1 · pith:VHBWVCL6new · submitted 2026-06-20 · 💻 cs.CY

Resume Screening, Fast and Slow: (Biased) AI Recommendations' Influence on Human Decision Making

Kyra Wilson , Mattea Sim , Anna-Maria Gueorguieva , Soham Chatterjee , Aylin Caliskan This is my paper

Pith reviewed 2026-06-26 10:50 UTC · model grok-4.3

classification 💻 cs.CY
keywords resume screeningAI biashuman-AI collaborationimplicit association testdecision makinghiring fairnesscognitive processes
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The pith

Biased AI recommendations alter the time people spend viewing resumes and influence hiring decisions

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

The paper tests how AI resume recommendations that carry social biases affect the time humans allocate to reviewing candidates. It reports that longer viewing time raises selection odds for non-recommended candidates by 3-4 percent and that participants spend up to 55.6 percent more time when no AI suggestions are present. Implicit association test scores taken before the task predict both viewing durations and whether candidates of different races receive equal attention. The authors conclude that these patterns show human decision processes are often insufficient to correct or oversee biased AI output in hiring.

Core claim

In an experiment on resume screening, AI recommendations change the duration people spend on each resume; non-recommended candidates receive more viewing time, which raises their selection probability by 3-4 percent, while IAT scores predict both the amount of time spent and the degree of racial parity in viewing times, indicating that human oversight may fail to neutralize AI bias.

What carries the argument

Time spent viewing resumes, treated as a measurable proxy for cognitive effort and fairness, together with its interaction with participants' IAT scores during human-AI collaboration.

If this is right

  • Non-recommended candidates gain a 3-4 percent higher selection chance when reviewers spend extra time on their resumes.
  • Removing AI recommendations increases average viewing time by as much as 55.6 percent.
  • Participants who complete an IAT before screening are more likely to allocate equal viewing time to candidates of different races.
  • IAT scores directly predict the duration of attention given during the human-AI screening task.

Where Pith is reading between the lines

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

  • Pre-task bias awareness exercises such as the IAT could be incorporated into hiring workflows to reduce unequal attention.
  • The results point to a need for system-level safeguards that do not rely solely on human reviewers to detect and correct AI bias.
  • Similar time-tracking methods might be tested in other high-stakes domains where AI assists decisions, such as loan approvals or medical triage.

Load-bearing premise

The time people spend looking at a resume accurately reflects the cognitive effort and fairness of their final hiring decision.

What would settle it

A replication in which viewing time shows no statistical link to selection outcomes or in which IAT scores fail to predict time differences across racial groups would undermine the central claim.

Figures

Figures reproduced from arXiv: 2606.22213 by Anna-Maria Gueorguieva, Aylin Caliskan, Kyra Wilson, Mattea Sim, Soham Chatterjee.

Figure 1
Figure 1. Figure 1: An example of the interfaces participants saw for the resume-screening task (left) and IAT (right). [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Most resumes were viewed for less than 60 seconds before being selected or not. (b) When candidates are not [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (a) These plots show the time spent (y-axis) viewing resumes based on the whether the candidate is perceived as [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (a) These plots show the time spent (y-axis) viewing Black vs. white resumes based on the kind of occupation in [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: This plot shows the change in time spent (y-axis) viewing resumes as participants’ IAT score changes (x-axis) based on [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: An example of the resumes participants saw for candidates in the resume-screening task. [PITH_FULL_IMAGE:figures/full_fig_p020_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Pictures used to represent racial groups in white vs. Black and white vs. Asian IATs. [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Estimated duration spent viewing resumes by participants’ self-reported recruiting experience. [PITH_FULL_IMAGE:figures/full_fig_p034_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Estimated duration spent viewing resumes by participants’ self-reported recruiting experience. [PITH_FULL_IMAGE:figures/full_fig_p035_9.png] view at source ↗
read the original abstract

AI is increasingly being used collaboratively with people to make decisions in high-stakes domains, but this new paradigm is still not well-understood in many respects -- particularly regarding how AI that replicates human social biases influences people's decision making processes and how that can influence outcomes. In this study, we analyzed the time people spend viewing candidate resumes from an experiment investigating biased AI resume screening to evaluate decision-making fairness and cognitive processes underlying human-AI collaboration. We found that spending more time viewing resumes corresponds to candidates' selection chance increasing by 3-4% if they are not recommended, and people may spend up to 55.6% longer viewing resumes when no AI recommendations are given. Furthermore, people who completed an implicit association test (IAT) before resume screening were significantly more likely to evaluate candidates of different races for the same amount of time, and their IAT scores were also predictive of the time spent in human-AI collaboration. These results demonstrate how people's decision-making processes can be insufficient for overseeing AI in high-stakes domains.

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

2 major / 0 minor

Summary. The paper reports an experiment analyzing time spent viewing resumes in a human-AI resume screening task. It claims that more viewing time increases selection probability by 3-4% for non-AI-recommended candidates, that people spend up to 55.6% longer viewing resumes without AI recommendations, that pre-task IAT completion leads to more equal evaluation times across races, and that IAT scores predict viewing time in human-AI collaboration. The authors conclude that these results show human decision-making processes are insufficient for overseeing biased AI in high-stakes domains.

Significance. If the proxy interpretations are validated, the work would contribute to understanding bias propagation in human-AI collaborative decision-making in hiring contexts. The experimental measurement of viewing time and IAT interactions provides a concrete empirical angle on oversight limitations, though the absence of direct links to decision accuracy limits its immediate applicability.

major comments (2)
  1. [Abstract] Abstract: The abstract reports directional effects (3-4% selection boost, 55.6% longer viewing) and IAT predictive effects but supplies no sample size, statistical model details, controls for multiple comparisons, or exclusion criteria. This prevents verification that the data support the stated claims and is load-bearing for all quantitative conclusions.
  2. [Abstract] Abstract (final sentence): The claim that the results demonstrate decision-making processes are insufficient for overseeing AI depends on treating time spent viewing resumes as a valid proxy for cognitive effort/fairness and IAT scores as capturing the relevant bias interaction. No evidence is provided that longer viewing time or IAT scores predict reduced bias or higher accuracy in final selections versus objective candidate merit; time could instead index resume length or difficulty. This interpretation is load-bearing for the central insufficiency conclusion.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful reading and constructive feedback on the abstract and its interpretive claims. We address each major comment below and indicate the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract reports directional effects (3-4% selection boost, 55.6% longer viewing) and IAT predictive effects but supplies no sample size, statistical model details, controls for multiple comparisons, or exclusion criteria. This prevents verification that the data support the stated claims and is load-bearing for all quantitative conclusions.

    Authors: We agree that the abstract should contain these details to allow independent assessment of the quantitative claims. The full manuscript reports the sample size, uses linear mixed-effects models with participant-level random effects, applies Bonferroni correction for multiple comparisons, and specifies exclusion criteria in the Methods. We will revise the abstract to include the sample size and a concise description of the modeling approach and corrections. revision: yes

  2. Referee: [Abstract] Abstract (final sentence): The claim that the results demonstrate decision-making processes are insufficient for overseeing AI depends on treating time spent viewing resumes as a valid proxy for cognitive effort/fairness and IAT scores as capturing the relevant bias interaction. No evidence is provided that longer viewing time or IAT scores predict reduced bias or higher accuracy in final selections versus objective candidate merit; time could instead index resume length or difficulty. This interpretation is load-bearing for the central insufficiency conclusion.

    Authors: The referee correctly notes that the manuscript does not provide direct evidence linking viewing time or IAT scores to reduced bias or improved accuracy against objective merit, and that alternative explanations (e.g., resume length or difficulty) are possible. We will revise the final sentence of the abstract to state the findings more narrowly as patterns in attention allocation that suggest limitations in human oversight, without claiming direct evidence of insufficient fairness in outcomes. We will also add a brief discussion of alternative interpretations of viewing time in the main text. revision: yes

Circularity Check

0 steps flagged

No circularity: purely experimental reporting with no derivation chain

full rationale

The paper reports measured outcomes from a human-subject experiment on resume viewing times, selection rates, and IAT correlations. No equations, fitted models, predictions, or first-principles derivations are claimed or present in the provided text. The central claim follows directly from the experimental data without any reduction to self-defined inputs, self-citations, or renamed known results. The proxy interpretations (time as effort, IAT as bias) raise questions of external validity but do not constitute circularity under the defined patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on domain assumptions from psychology about what IAT scores measure and what viewing time indicates about decision processes; no free parameters or new entities are introduced.

axioms (2)
  • domain assumption The implicit association test measures implicit racial bias that influences time allocation during resume screening.
    Invoked to interpret the finding that IAT scores predict viewing times across racial groups.
  • domain assumption Time spent viewing a resume is a reliable indicator of cognitive processing and decision fairness.
    Used to link longer viewing to increased selection chance for non-recommended candidates.

pith-pipeline@v0.9.1-grok · 5731 in / 1286 out tokens · 33137 ms · 2026-06-26T10:50:10.265355+00:00 · methodology

discussion (0)

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Reference graph

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