Journal of Applied Psychology 2024

Automated speech recognition bias in personnel selection: The case of automatically scored job interviews.

Louis Hickman¹; Markus Langer²; Rachel Saef³; Louis Tay⁴

¹ Virginia Tech · ² University of Freiburg · ³ Northern Illinois University · ⁴ Department of Psychological Sciences, Purdue University

Abstract

s = 1,014 and 414), and then we train and test models using these different transcripts to score multiple constructs. We observed significant bias in the commercial ASR services across nearly all comparisons, with the magnitude of bias differing across the ASR services. However, the transcription bias did not translate into meaningful measurement bias for the ML interview scores-whether in terms of differential convergent correlations or means. We discuss what these results mean for the nature of bias, fairness, and validity of ML models for scoring verbal open-ended responses. (PsycInfo Database Record

DOI

10.1037/apl0001247

Sources

openalex

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