ReviewPatterns (New York, N.Y.)2022
A Silicon Valley love triangle: Hiring algorithms, pseudo-science, and the quest for auditability.
Review in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Examining the assumptions of AI hiring assessments and their impact on job seekers' autonomy over self-representation.AI & society · 2025Article
- The reanimation of pseudoscience in machine learning and its ethical repercussions.Patterns (New York, N.Y.) · 2024Review
- Michael is better than Mehmet: exploring the perils of algorithmic biases and selective adherence to advice from automated decision support systems in hiring.Frontiers in psychology · 2024Article
- Gender equity in hiring: examining the effectiveness of a personality-based algorithm.Frontiers in psychology · 2023Article
- An external stability audit framework to test the validity of personality prediction in AI hiring.Data mining and knowledge discovery · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this perspective, we develop a matrix for auditing algorithmic decision-making systems (ADSs) used in the hiring domain. The tool is a socio-technical assessment of hiring ADSs that is aimed at surfacing the underlying assumptions that justify the use of an algorithmic tool and the forms of knowledge or insight they purport to produce. These underlying assumptions, it is argued, are crucial for assessing not only whether an ADS works "as intended," but also whether the intentions with which the tool was designed are well founded. Throughout, we contextualize the use of the matrix within current and proposed regulatory regimes and within emerging hiring practices that incorporate algorithmic technologies. We suggest using the matrix to expose underlying assumptions rooted in pseudo-scientific essentialized understandings of human nature and capability and to critically investigate emerging auditing standards and practices that fail to address these assumptions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.