Exploring Bias in Artificial Intelligence in Digital Transformation and the Datafied Future of Work
Year: 2025
Type: book-chapter
Abstract: This chapter explores ‘algorithmic management’ (Meijerink & Bondarouk, 2023), automated decision-making in human resource management (HRM) in the context of the datafied future of work. This chapter argues that the coding element of algorithmic decision-making is a fundamental and crucial element of bias and (intersectional) inequality perpetuation in contemporary organisational decision-making. Further, the code is critically posited as the (veiled, outsourced) driver for inequality and bias perpetuation, highlighting the need for diversity in tech as a prerequisite for coding for equity. This chapter draws on a number of real-life examples, including ‘Correctional Offender Management Profiling for Alternative Sanctions (COMPAS’) the algorithmic decision-making tool used by US courts to assess the probability of re-offending, and Amazon’s sexist AI hiring tool. A conceptual model is offered which outlines the factors which need to be considered for the development of (intersectionally) inclusive and sustainable AI in digital transformation and the datafied future of work. Additionally, several practical recommendations devised to ensure that algorithms and models are developed more inclusively and to drive fairer outcomes are provided.
DOI: 10.57711/che6-2j81
Author: Yarrow E
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W6973866923
FWCI:
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