Hiring Fairly in the Age of Algorithms

Langenkamp, Max; Costa, Allan; Cheung, Chris

doi:10.2139/ssrn.3723046

Cited by 14 publications

(5 citation statements)

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“…The bias stemmed from training the AI system on predominantly male employees' CVs (Beneduce, 2020). Accordingly, the recruitment algorithm perceived this biased model as indicative of success, resulting in discrimination against female applicants (Langenkamp et al 2019). The algorithm even downgraded applicants with keywords such as "female" (Faragher, 2019).…”

Section: Theme III Which Algorithmic Recruitment Discrimination Existsmentioning

confidence: 99%

Ethics and discrimination in artificial intelligence-enabled recruitment practices

Chen

2023

Humanit Soc Sci Commun

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This study aims to address the research gap on algorithmic discrimination caused by AI-enabled recruitment and explore technical and managerial solutions. The primary research approach used is a literature review. The findings suggest that AI-enabled recruitment has the potential to enhance recruitment quality, increase efficiency, and reduce transactional work. However, algorithmic bias results in discriminatory hiring practices based on gender, race, color, and personality traits. The study indicates that algorithmic bias stems from limited raw data sets and biased algorithm designers. To mitigate this issue, it is recommended to implement technical measures, such as unbiased dataset frameworks and improved algorithmic transparency, as well as management measures like internal corporate ethical governance and external oversight. Employing Grounded Theory, the study conducted survey analysis to collect firsthand data on respondents’ experiences and perceptions of AI-driven recruitment applications and discrimination.

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Section: Theme III Which Algorithmic Recruitment Discrimination Existsmentioning

confidence: 99%

Ethics and discrimination in artificial intelligence-enabled recruitment practices

Chen

2023

Humanit Soc Sci Commun

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“…They may also avoid incorporating programmer's suggestions, as it would require extensive work to accommodate them. This is exemplified by Amazon's initial resistance to dismantling its gender-biased hiring algorithm (e.g., Iriondo 2018), as correcting the algorithm required time, money, and labor (Langenkamp et al 2020). Indeed, existing literature has established that leaders' behaviors and attitudes can reduce their subordinates' general propensity to express voice or increase the likelihood they will engage in organizational silence (Detert and Treviño 2010, Morrison 2023, Taiyi Yan et al 2022.…”

Section: Domain-specific Implicit Voice Theoriesmentioning

confidence: 99%

The Manager in the Machine: Organizational Priorities Influence AI Programmer’s Ability to Design Fair Models

Osborne,

Cratsley,

Massey

et al. 2024

Preprint

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Artificial Intelligence (AI) models can improve everyday life by informing our decisions. Yet, experts have warned that these useful tools hold consequential biases that can perpetuate inequality in our decision-making. Computer scientists have demonstrated how the data used to train these models hold bias, while psychologists have shown that programmers’ own biases influence how (or if) they address this bias in the data. Yet both bodies of research do not fully account for an important fact: many of the programmers who construct these models are situated in organizations, and thus have to navigate organizational dynamics that influence how they construct AI models. We thus integrate theoretical concepts from computer science, psychology, and management to develop a novel theoretical model to illustrate how a construct developed in computer science – the “fairness vs. accuracy tradeoff” in model construction – impacts programmers’ decision-making when constructing AI models. We contend that programmers ultimately have to prioritize fairness or accuracy in their decision-making, and that they determine which priority to uphold based on their perceptions of their organizational leader’s priorities (a previously undertheorized dynamic in research on bias in machine learning). These perceptions shape their decision to express ethical voice about the model’s outcomes, an important predictor of the amount of bias the model holds. We further draw parallels between this tradeoff for organizations in developing AI and broader organizational contexts. This research contributes to management research by drawing further attention to an important organizational issue (developing fair AI models) using employee voice.

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“…The negative impact of hiring algorithms can be mitigated by adopting measures that provide greater transparency for employers to the public, which would allow State institutions and human rights activists to hold employers accountable for hiring based on biased hiring Direito e Democracia na Sociedade Contemporânea algorithms (LAGENKAMP, COSTA;CHEUNG, 2020). In this sense, employers using such algorithmic tools would have to be required by Law to adopt technological structures for automated contracting transparency (in the so-called " regulation-by-design"), as well as to publish reports of algorithmic transparency.…”

Section: Direito E Democracia Na Sociedade Contemporâneamentioning

confidence: 99%

Artificial Intelligence and Labor

Fornasier

2021

Rev. Fac. Mineira de Direito

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This article studies the impacts that the new economy, resulting from automation, represents for Law. Its hypothesis is that a new social security, based on universal basic income, funded by taxing the use of automation tools, should replace systems based mainly on the employment relationship; and principles related to transparency, explicability and non-discrimination should create obligations for developers and users of AI-powered worker selection tools. Methodology: hypothetical-deductive procedure method, with a transdisciplinary and qualitative approach, and bibliographic review research technique. Results: i) Labor regulation must be planned beyond substitution, focusing on a new economy, in which formal jobs, inserted in a paradigm of social and economic protection, are eroding, and the great challenge will be to protect decent work standards concurrently with the enlargement of dignity for non employed workers; ii) A universal basic income funded by taxes on automation would be interesting, but problematic from the point of view of solidarity in its costing, since the stress of national economies can cause fear on governments. And globally, such taxation, if adopted differently, can cause great tax competition between countries; iii) Transparency obligations are necessary to mitigate bias in hiring tools, but not self-sufficient, because the bias is complex, mainly due to the multiplicity of discriminatory factors and to the opacity of the logic in machine learning.

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Hiring Fairly in the Age of Algorithms

Cited by 14 publications

References 0 publications

Ethics and discrimination in artificial intelligence-enabled recruitment practices

Ethics and discrimination in artificial intelligence-enabled recruitment practices

The Manager in the Machine: Organizational Priorities Influence AI Programmer’s Ability to Design Fair Models

Artificial Intelligence and Labor

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