Scientific, Legal, and Ethical Concerns about AI-Based Personnel Selection Tools: A Call to Action

Abstract

Organizations are increasingly turning toward personnel selection tools that rely on artificial intelligence (AI) technologies and machine learning algorithms that, together, intend to predict the future success of employees better than traditional tools. These new forms of assessment include online games, video-based interviews, and the use of large amounts of big data pulled from many sources, including test responses, test-taking behavior, applications, resumes, and social media. Speedy processing, lower costs, convenient access, and applicant engagement are often and rightfully cited as the practical advantages for using these selection tools. At the same time, however, these tools raise serious concerns about their effectiveness in terms their conceptual relevance to the job; their basis in a job analysis to ensure job relevancy; their measurement characteristics (reliability and stability); their validity in predicting employee-relevant outcomes; their evidence and normative information being updated appropriately; and the associated ethical concerns around what information is being represented to employers and told to job candidates. This paper explores these concerns, concluding with an urgent call to industrial and organizational psychologists to extend existing professional standards for employment testing to these new AI and machine learning based forms of testing, including standards and requirements for their documentation.

Understanding and shaping the future of work with self-determination theory

“…These benefits have driven the widespread adoption of technology in recruitment practices 77 , but they have also attracted criticism. For example, the use of AI to analyse candidate data (such as CVs, social media profiles, text-based responses to interview questions, and videos) 78 raises concerns about the relevance of data being collected for selecting employees, transparency in how the data are used, and biases in selection based on these data 79 .…”

Individual differences at play: An investigation into measuring Big Five personality facets with game‐based assessments

“…For example, game-based assessments (GBAs) of personality traits are becoming increasingly available and more popular in organizations, which, in contrast to gamification (i.e., adding some game elements to nongame settings), consist of full games used to assess specific constructs (Landers, 2019). Although test vendors may claim that GBAs measure personality traits reliably and fairly while predicting organizational outcomes, researchers in support of those vendors should follow established professional guidelines, such as the Society for Industrial and Organizational Psychology (2018), for collecting and evaluating the evidence for such claims (Tippins et al, 2021).…”

A meta-analysis on air traffic controllers selection: cognitive and non-cognitive predictors

“…Some papers have been published on the subject with ATCOs (Borack, 1995) but with the democratization and improvement of machine learning, a new interest could emerge for these algorithms. Inspite of this, a simple method can be better than complex ones (Nusinovici et al, 2020) and explicability should be developed: a "black-box" is difficult to certify (Tippins, Oswald, & McPhail, 2021). Based on Pareto-optimality concept (De Corte, Lievens, & Sackett, 2007), elaboration of composite scores and their weightings may be improved in order to consider validity, adverse impact and constraints (De Corte, Sackett, & Lievens, 2010).…”