Age Discrimination in Hiring

Neumark, David

doi:10.3368/jhr.0420-10831r1

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…Age-based discrimination is also one of the most relevant issues in hiring (Abrams et al, 2016;Baert, 2018;Lössbroek et al, 2021;Neumark et al, 2017;Zaniboni et al, 2019). Although this form of discrimination can affect both "too young" and "too old" candidates, current literature states that older applicants tend to have worse job application chances than younger applicants (Lössbroek et al, 2021;Neumark, 2021;Zaniboni et al, 2019). Reasons for this can be stereotypical perceptions of older candidates, such as poorer trainability (Richardson et al, 2013).…”

Section: Bias and Discrimination In Hiringmentioning

confidence: 99%

“…Comparing the participants with and without HR experience, those with HR experience mentioned age more often as a reason for their decision. If we assume that a candidate was selected mainly because of their age and not because of a certain skillset, we consider this a case of age discrimination (Baert, 2018;Neumark, 2021;Richardson et al, 2013;Zaniboni et al, 2019). However, AI-based recommendations showed a positive effect on the selection of an older candidate for both groups.…”

Section: Ai Recommendations Can Reduce Ageand Gender-based Discrimina...mentioning

confidence: 99%

“…Females or mothers (Baert, 2018;Ruffle & Shtudiner, 2015) Age Too young or old (Baert, 2018;Neumark, 2021;Richardson et al, 2013;Zaniboni et al, 2019)…”

Section: Appendixmentioning

confidence: 99%

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Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring

et al. 2022

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Assuming that potential biases of Artificial Intelligence (AI)-based systems can be identified and controlled for (e.g., by providing high quality training data), employing such systems to augment human resource (HR)-decision makers in candidate selection provides an opportunity to make selection processes more objective. However, as the final hiring decision is likely to remain with humans, prevalent human biases could still cause discrimination. This work investigates the impact of an AI-based system’s candidate recommendations on humans’ hiring decisions and how this relation could be moderated by an Explainable AI (XAI) approach. We used a self-developed platform and conducted an online experiment with 194 participants. Our quantitative and qualitative findings suggest that the recommendations of an AI-based system can reduce discrimination against older and female candidates but appear to cause fewer selections of foreign-race candidates. Contrary to our expectations, the same XAI approach moderated these effects differently depending on the context.

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Section: Bias and Discrimination In Hiringmentioning

confidence: 99%

Section: Ai Recommendations Can Reduce Ageand Gender-based Discrimina...mentioning

confidence: 99%

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Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring

et al. 2022

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“…Finland operates extensive, and comparatively generous, earnings-replacement benefits for the unemployed, for people with health problems, for those with care responsibilities and for early retirees. As a complement to these primary earnings-replacement benefits, means-tested support is mainly intended as a short-term benefit of last resort (Ministry of Social Affairs and Health, 1997 [27]). However, in recent years social assistance, alongside housing benefits, are increasingly being used to supplement the primary basic level social security benefits.…”

Section: Increased Reliance On Social Assistance To Supplement Earnin...mentioning

confidence: 99%

Faces of joblessness in Finland

2021

OECD Social, Employment and Migration Working Papers

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Working Papers should not be reported as representing the official views of the OECD or of its member countries. The opinions expressed and arguments employed are those of the author(s).Working Papers describe preliminary results or research in progress by the author(s) and are published to stimulate discussion on a broad range of issues on which the OECD works. Comments on Working Papers are welcomed, and may be sent to

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“…Ideally, gender, age or skin colour 2 should play no role in the hiring of candidates, but there is evidence from the economics literature to suggest that this isn't the case (Bertrand and Mullainathan, 2004;Goldin and Rouse, 2000;Neumark, 2020). Either because of prejudice (Becker, 1957), or 'statistical' processes that break along ageist, racial or gendered lines (Arrow, 1972;Phelps, 1972) it is possible that the non-productivity related characteristics of the candidate impacts hiring.…”

Section: Introductionmentioning

confidence: 99%