Reference health

Algorithmic Hiring and Diversity: Reducing Human-Algorithm Similarity for Better Outcomes

https://doi.org/10.2139/ssrn.4431080
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17/17 checkable references clean · checked 2026-09-05

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

23 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 17 checked references that resolve
resolves10.1145/3531146.3533178
Multi Stage Screening: Enforcing Fairness and Maximizing Efficiency in a Pre-Existing Pipeline
resolves10.1177/0001839216682728
Leaning Out: How Negative Recruitment Experiences Shape Women’s Decisions to Compete for Executive Roles
resolves10.1145/3442188.3445930
The Effect of the Rooney Rule on Implicit Bias in the Long Term
resolves10.1089/big.2016.0047
Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments
resolves10.1287/opre.33.2.427
Limits for the Precision and Value of Information from Dependent Sources
resolves10.1145/2090236.2090255
Fairness through awareness
resolves10.2307/257055
THE AFFIRMATIVE ACTION STIGMA OF INCOMPETENCE: EFFECTS OF PERFORMANCE INFORMATION AMBIGUITY.
resolves10.1016/j.jebo.2020.11.014
Diversity and the timing of preference in hiring decisions
resolves10.5465/amj.2011.0940
The Stigma of Affirmative Action: A Stereotyping-Based Theory and Meta-Analytic Test of the Consequences for Performance
resolves10.1146/annurev-statistics-042720-125902
Algorithmic Fairness: Choices, Assumptions, and Definitions
resolves10.1609/hcomp.v7i1.5281
What You See Is What You Get? The Impact of Representation Criteria on Human Bias in Hiring
resolves10.1145/3351095.3372828
Mitigating bias in algorithmic hiring
resolves10.1002/hrm.21837
The adoption of chief diversity officers among S&P 500 firms: Institutional, resource dependence, and upper echelons accounts
resolves10.25300/MISQ/2021/16559
When the Machine Meets the Expert: An Ethnography of Developing AI for Hiring
resolves10.1080/03610926.2018.1439962
Matching a correlation coefficient by a Gaussian copula
resolves10.1145/3038912.3052660
Fairness Beyond Disparate Treatment & Disparate Impact
resolves10.1145/3278721.3278779
Mitigating Unwanted Biases with Adversarial Learning
The 23 references without a DOI — listed, not checked
no DOI — not checkedRejection Communication and Women's Job-Search Persistence
no DOI — not checkedFair Pipelines
no DOI — not checkedref8
no DOI — not checkedref9
no DOI — not checkedFairness Under Composition
no DOI — not checked)
no DOI — not checkedref13
no DOI — not checkedref14
no DOI — not checkedref16
no DOI — not checkedFair Selection through Kernel Density Estimation
no DOI — not checkedref18
no DOI — not checkedFair Sequential Selection Using Supervised Learning Models
no DOI — not checkedref20
no DOI — not checkedref21
no DOI — not checkedAn Introduction to Copulas
no DOI — not checkedAffirmative Action: Past, Present, and Future
no DOI — not checkedref30
no DOI — not checkedDoes Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring
no DOI — not checkedHow to Fine-Tune BERT for Text Classification?
no DOI — not checkedAttention Is All You Need
no DOI — not checkedref37
no DOI — not checkedBig Bird: Transformers for Longer Sequences
no DOI — not checkedLearning Fair Representations
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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