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 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.

How Well Can an AI Chatbot Infer Personality? Examining Psychometric Properties of Machine-inferred Personality Scores

“…We thus encourage computer scientists and psychologists to work together to figure out substantive meanings of high-dimension vectors extracted from various DL models, which shall allow for a fuller and more direct investigation of the content validity of machineinferred personality scores. This is aligned with the current trend that explainable AI has been a growing area of research in ML (Tippins et al, 2021).…”

“…It is generally referred to as artificial intelligence (AI)-based personality assessment. This new form of assessment can be distinguished from traditional assessment in three ways: technologies, types of data, and algorithms (Tippins et al, 2021). Data collected via diverse technological platforms (e.g., social media and video interviews) have been used to obtain an assortment of personality-relevant data (digital footprints) such as facial expression (Suen et al, 2019), smartphone data (Chittaranjan et al, 2013), interview responses (Hickman et al, 2022), and online chat scripts (Li et al, 2017).…”

LinkedIn‐based assessments of applicant personality, cognitive ability, and likelihood of organizational citizenship behaviors: Comparing self‐, other‐, and language‐based automated ratings

“…More recently, research has started to examine the use of technology to automatize the assessment of personality. Some authors have suggested that automated assessments of personality have the potential to be more consistent and accurate than human judgments, but argued more research is needed to examine potential biases or legal and ethical issues associated with fairness or privacy (Alexander et al, 2020;Tippins et al, 2021). It is also important to emphasize that automated assessments (for instance based on natural language processing techniques) can take a number of forms, and their reliability, validity, and fairness depends on how the assessment model was designed, developed, or tested (Landers & Behrend, 2022).…”

Better explaining the benefits why AI? Analyzing the impact of explaining the benefits of AI‐supported selection on applicant responses

“…However, recruiters should still keep in mind when implementing AI-supported selection tools that AI-supported selection tools can lead to unfair treatment if the underlying training data set is unbalanced or contains discrimination, or if the system is poorly designed (e.g., Köchling & Wehner, 2020;Köchling et al, 2021). It is therefore important that the AI-supported systems are properly designed, trained, validated, and monitored (Tippins et al, 2021).…”