The diversity-validity dilemma is an issue that industrial-organisational psychologists have faced for decades. Describing the trade-off between choosing selection procedures that are the most predictive of future job performance and choosing procedures that result in less adverse impact, referring to differential hiring rates for different subgroups based on characteristics such as sex/gender and race/ethnicity. In this paper, we explore at why the diversity-validity dilemma is significant and how it can be overcome.
Artificial intelligence (AI) and machine learning have opened up new opportunities for psychological assessments and psychometrics. These technologies have also transformed assessments; game and image-based assessments are increasingly being used in place of traditional self-report measures. We no longer have to completely rely on self-report data and can instead rapidly infer insights from a number of sources such as verbal and non-verbal behaviour in videos and the language used in social media posts.
AI is increasingly being used in talent management, with research from the Society for Human Resource Management finding that almost 25% of companies are using AI to support their HR practices, including recruitment and hiring. In this paper, we give an overview of some novel assessment formats that use AI in their scoring.
The Discussion Paper clarifies how existing regulations and guidance -- including on risk management, consumer protection and data protection -- applies to the use of AI. It seeks feedback on whether new regulations are required.
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