As the popularity of biodata in selection assessments grew in the 1980s and into the 1990s, the field of industrial and organizational psychology witnessed many attempts to develop biodata theories and guide the development of biodata items. The insights that emerged from this body of research are increasingly relevant in the current era of big data, artificial intelligence (AI), and machine learning. More than ever, AI and machine learning are being used to score candidates and make hiring recommendations. Many organizations are using data-driven approaches to develop machine learning and AI algorithms, which are frequently atheoretical, based on correlations or pattern recognition, and can indirectly propagate bias in personnel selection or hiring. The underlying problems with popular AI-powered assessments and algorithmic are, in many ways, similar to the historical problems with biodata. As such, the present paper examines the long history of biodata use in personnel assessment, drawing parallels between past and present, and identifying lessons learned and their implications for applications of machine learning and artificial intelligence in the hiring process. We provide recommendations for the problems faced today based on the lessons learned from historical biodata research.
Sodhi, Ketaki and Cubrich, Marc
"Biographical Data and Black Box Empiricism: Lessons Learned for Algorithmic Assessments in Personnel Selection,"
Psychology from the Margins: Vol. 3
, Article 5.
Available at: https://ideaexchange.uakron.edu/psychologyfromthemargins/vol3/iss1/5