TY - JOUR
T1 - Examining the assumptions of AI hiring assessments and their impact on job seekers’ autonomy over self-representation
AU - Aizenberg, Evgeni
AU - Dennis, Matthew J.
AU - van den Hoven, Jeroen
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - AI
KW - Algorithm
KW - Autonomy
KW - Dignity
KW - Hiring
KW - Self-representation
UR - http://www.scopus.com/inward/record.url?scp=85174606317&partnerID=8YFLogxK
U2 - 10.1007/s00146-023-01783-1
DO - 10.1007/s00146-023-01783-1
M3 - Article
AN - SCOPUS:85174606317
SN - 0951-5666
VL - 40 (2025)
SP - 919
EP - 927
JO - AI and Society
JF - AI and Society
IS - 2
ER -