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Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provides partial treatment, often constrained by two competing narratives, optimistically focused on replacing biased recruiter decisions or pessimistically pointing to the automation of discrimination. Whether, and more importantly
what types of
, algorithmic hiring can be less biased and more beneficial to society than low-tech alternatives currently remains unanswered, to the detriment of trustworthiness. This multidisciplinary survey caters to practitioners and researchers with a balanced and integrated coverage of systems, biases, measures, mitigation strategies, datasets, and legal aspects of algorithmic hiring and fairness. Our work supports a contextualized understanding and governance of this technology by highlighting current opportunities and limitations, providing recommendations for future work to ensure shared benefits for all stakeholders.
Academic Publication Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey
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Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey
Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequali...
Addressing AI Algorithmic Bias in Health Care
This Viewpoint discusses the bias that exists in artificial intelligence (AI) algorithms used in health care despite recent federal rules to prohibit discriminatory outcomes from AI and recommends ...
Fairness in Machine Learning: A Survey
When Machine Learning technologies are used in contexts that affect citizens, companies as well as researchers need to be confident that there will not be any unexpected social implications, such a...
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey
This article provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias mitigation for...
Strategic Human Resource Management in the Era of Algorithmic Technologies: Key Insights and Future Research Agenda
ABSTRACTThis article presents a contemporary review of human resource management (HRM) research on algorithmic technologies, including artificial intelligence, machine learning, and natural languag...
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What is the core focus of the research titled 'Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey'?
This literature focuses on: Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this domain due to its high stakes and structural inequalities. Unfortunately, most work in this space provi...
Which startups are commercializing the technology behind Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey?
Products like aperture are bringing this to market. Their focus is: hiring is broken. we're building the fix..
What other academic literature is closely related to 'Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey'?
Yes, highly correlated activity was mapped. An entry titled 'Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey' discusses this: Employers are adopting algorithmic hiring technology throughout the recruitment pipeline. Algorithmic fairness is especially applicable in this dom...
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