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E. Aizenberg
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Correction to
The Ethics of AI in Human Resources (Ethics and Information Technology, (2022), 24, 3, (25), 10.1007/s10676-022-09653-y)
The original article includes few corrections. They are as follows: The affiliation of the authors was published as: Department of Industrial Engineering and Innovation Sciences (IE&IS), Philosophy&Ethics Capacity Group, TU/e, Eindhoven, NL, Netherlands. It must be published as: Matthew J, Dennis 1. Department of Industrial Engineering&Innovation Sciences (IE&IS), Philosophy&Ethics Capacity Group, TU/e, Eindhoven, The Netherlands. Evgeni Aizenberg 2, 3 Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands AiTech Interdisciplinary Research Program on Meaningful Human Control over AI, Delft University of Technology, Delft, The Netherlands In original article, Footnotes 1 and 2 must be switched. It must be published as: Footnote 1: ModernHire claims a 70% reduction in interview-tohire ratio (2021). HireVue claims a 90% decrease from initial application to hire (2021). To get an idea of what this amounts to in practice, see Forbes’ interview of Leena Nair, Unilever’s chief of HR. Nair claims that approximately “70,000 person-hours of interviewing and assessing candidates had been cut, thanks to their automated screening system” (Forbes 2018). Footnote 2: Pymetrics urges employers not to “judge a job seeker by their resume alone.” Instead, the company proposes that their “objective behavioural data that measures a job seeker’s true potential” are better predictors of future productivity rather than “focusing on backward-looking resumes orself-reported questionnaires” The following reference must be deleted. Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., and Sutherland, W. (2021). Algorithmic management in a work context. Big Data and Society, 8(2), 20539517211020332. https://doi. org/10.1177/20539517211020332 The original article has been corrected.
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The original article includes few corrections. They are as follows: The affiliation of the authors was published as: Department of Industrial Engineering and Innovation Sciences (IE&IS), Philosophy&Ethics Capacity Group, TU/e, Eindhoven, NL, Netherlands. It must be published as: Matthew J, Dennis 1. Department of Industrial Engineering&Innovation Sciences (IE&IS), Philosophy&Ethics Capacity Group, TU/e, Eindhoven, The Netherlands. Evgeni Aizenberg 2, 3 Department of Intelligent Systems, Delft University of Technology, Delft, The Netherlands AiTech Interdisciplinary Research Program on Meaningful Human Control over AI, Delft University of Technology, Delft, The Netherlands In original article, Footnotes 1 and 2 must be switched. It must be published as: Footnote 1: ModernHire claims a 70% reduction in interview-tohire ratio (2021). HireVue claims a 90% decrease from initial application to hire (2021). To get an idea of what this amounts to in practice, see Forbes’ interview of Leena Nair, Unilever’s chief of HR. Nair claims that approximately “70,000 person-hours of interviewing and assessing candidates had been cut, thanks to their automated screening system” (Forbes 2018). Footnote 2: Pymetrics urges employers not to “judge a job seeker by their resume alone.” Instead, the company proposes that their “objective behavioural data that measures a job seeker’s true potential” are better predictors of future productivity rather than “focusing on backward-looking resumes orself-reported questionnaires” The following reference must be deleted. Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., and Sutherland, W. (2021). Algorithmic management in a work context. Big Data and Society, 8(2), 20539517211020332. https://doi. org/10.1177/20539517211020332 The original article has been corrected.
In this paper, we examine the epistemological and ontological assumptions algorithmic hiring assessments make about job seekers’ attributes (e.g., competencies, skills, abilities) and the ethical implications of these assumptions. Given that both traditional psychometric hiring assessments and algorithmic assessments share a common set of underlying assumptions from the psychometric paradigm, we turn to literature that has examined the merits and limitations of these assumptions, gathering insights across multiple disciplines and several decades. Our exploration leads us to conclude that algorithmic hiring assessments are incompatible with attributes whose meanings are context-dependent and socially constructed. Such attributes call instead for assessment paradigms that offer space for negotiation of meanings between the job seeker and the employer. We argue that in addition to questioning the validity of algorithmic hiring assessments, this raises an often overlooked ethical impact on job seekers’ autonomy over self-representation: their ability to directly represent their identity, lived experiences, and aspirations. Infringement on this autonomy constitutes an infringement on job seekers’ dignity. We suggest beginning to address these issues through epistemological and ethical reflection regarding the choice of assessment paradigm, the means to implement it, and the ethical impacts of these choices. This entails a transdisciplinary effort that would involve job seekers, hiring managers, recruiters, and other professionals and researchers. Combined with a socio-technical design perspective, this may help generate new ideas regarding appropriate roles for human-to-human and human–technology interactions in the hiring process.
...
In this paper, we examine the epistemological and ontological assumptions algorithmic hiring assessments make about job seekers’ attributes (e.g., competencies, skills, abilities) and the ethical implications of these assumptions. Given that both traditional psychometric hiring assessments and algorithmic assessments share a common set of underlying assumptions from the psychometric paradigm, we turn to literature that has examined the merits and limitations of these assumptions, gathering insights across multiple disciplines and several decades. Our exploration leads us to conclude that algorithmic hiring assessments are incompatible with attributes whose meanings are context-dependent and socially constructed. Such attributes call instead for assessment paradigms that offer space for negotiation of meanings between the job seeker and the employer. We argue that in addition to questioning the validity of algorithmic hiring assessments, this raises an often overlooked ethical impact on job seekers’ autonomy over self-representation: their ability to directly represent their identity, lived experiences, and aspirations. Infringement on this autonomy constitutes an infringement on job seekers’ dignity. We suggest beginning to address these issues through epistemological and ethical reflection regarding the choice of assessment paradigm, the means to implement it, and the ethical impacts of these choices. This entails a transdisciplinary effort that would involve job seekers, hiring managers, recruiters, and other professionals and researchers. Combined with a socio-technical design perspective, this may help generate new ideas regarding appropriate roles for human-to-human and human–technology interactions in the hiring process.
Journal article
(2022)
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L. Cavalcante Siebert, M.L. Lupetti, M.J. van den Hoven, D. Forster, R.L. Lagendijk, E. Aizenberg, N.W.M. Beckers, A. Zgonnikov, H.M. Veluwenkamp, D.A. Abbink, Elisa Giaccardi, G.J.P.M. Houben, C.M. Jonker
How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.
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How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.
In the age of Big Data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance pricing, and many more aspects of our lives. Artificial intelligence (AI) systems can help us make evidence-driven, efficient decisions, but can also confront us with unjustified, discriminatory decisions wrongly assumed to be accurate because they are made automatically and quantitatively. It is becoming evident that these technological developments are consequential to people’s fundamental human rights. Despite increasing attention to these urgent challenges in recent years, technical solutions to these complex socio-ethical problems are often developed without empirical study of societal context and the critical input of societal stakeholders who are impacted by the technology. On the other hand, calls for more ethically and socially aware AI often fail to provide answers for how to proceed beyond stressing the importance of transparency, explainability, and fairness. Bridging these socio-technical gaps and the deep divide between abstract value language and design requirements is essential to facilitate nuanced, context-dependent design choices that will support moral and social values. In this paper, we bridge this divide through the framework of Design for Values, drawing on methodologies of Value Sensitive Design and Participatory Design to present a roadmap for proactively engaging societal stakeholders to translate fundamental human rights into context-dependent design requirements through a structured, inclusive, and transparent process.
...
In the age of Big Data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance pricing, and many more aspects of our lives. Artificial intelligence (AI) systems can help us make evidence-driven, efficient decisions, but can also confront us with unjustified, discriminatory decisions wrongly assumed to be accurate because they are made automatically and quantitatively. It is becoming evident that these technological developments are consequential to people’s fundamental human rights. Despite increasing attention to these urgent challenges in recent years, technical solutions to these complex socio-ethical problems are often developed without empirical study of societal context and the critical input of societal stakeholders who are impacted by the technology. On the other hand, calls for more ethically and socially aware AI often fail to provide answers for how to proceed beyond stressing the importance of transparency, explainability, and fairness. Bridging these socio-technical gaps and the deep divide between abstract value language and design requirements is essential to facilitate nuanced, context-dependent design choices that will support moral and social values. In this paper, we bridge this divide through the framework of Design for Values, drawing on methodologies of Value Sensitive Design and Participatory Design to present a roadmap for proactively engaging societal stakeholders to translate fundamental human rights into context-dependent design requirements through a structured, inclusive, and transparent process.
Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: A feasibility study (vol 79, pg 1127, 2018)
Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: A feasibility study (Magn Reson Med. 2018; 79:1127‐1134)
Journal article
(2019)
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Evgeni Aizenberg, Edgar A.H. Roex, Wouter P. Nieuwenhuis, Lukas Mangnus, Annette H.M. van der Helm-van Mil, Monique Reijnierse, Johan L. Bloem, Boudewijn P.F. Lelieveldt, Berend C. Stoel
In Magn Reson Med. 2018; 79:1127-1134, the acquisition matrix of the coronal T1-weighted sequence acquired prior to contrast injection was mistyped. The correct acquisition matrix is 388 × 288 instead of: 388 × 88. We apologize for any inconvenience this may have caused to the readers.
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In Magn Reson Med. 2018; 79:1127-1134, the acquisition matrix of the coronal T1-weighted sequence acquired prior to contrast injection was mistyped. The correct acquisition matrix is 388 × 288 instead of: 388 × 88. We apologize for any inconvenience this may have caused to the readers.