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Full Title:
Conceptualizing and assessing bias in the 21^st century: From traditional assessments to machine learning algorithms

Abstract:
The use of big data, artificial intelligence, and machine learning has become pervasive across multiple industries and organizations. One critical concern is algorithm bias, especially in ones used to make high-stakes decisions. In this talk, I will trace how the concept of bias has evolved from psychometrics (i.e., traditional testing algorithms) to machine learning and show how my work has advanced some of the conceptualization, testing, and assessment of bias across these endeavors, culminating in my current thinking on Machine Learning Measurement Bias and beyond. Part of this work is funded by the National Science Foundation.

Bio: 
Louis Tay is William C. Byham Professor of Industrial-Organizational Psychology at Purdue University. His substantive research interests include well-being (subjective well-being, psychological well-being), character strengths, and vocational interests. His methodological research interests include measurement, item response theory, latent class modeling, multilevel analysis, and data science. He is a co-editor of the books Big Data in Psychological Research(APA Books), Handbook of Well-Being (DEF Publishers), Handbook of Positive Psychology Assessment (Hogrefe), Oxford Handbook of the Positive Humanities (Oxford), and Technology and Measurement around the Globe(Cambridge). He founded the tech startup ExpiWell, which advances the science and capture of daily life experiences.

Citations:
Woo, S. E., Tay, L., & Proctor, R. W. (Eds.) (2020). Big Data in  Psychological Research. American Psychological Association 

Tay, L., Woo, S. E., & Behrend, T. (2023). Technology and Measurement: Research and Practices around the Globe. Cambridge University Press.

Tay^*, L., Woo, S. E., Hickman, L., Booth, B., D’Mello, S. D. (2022). A conceptual framework for investigating and mitigating machine learning measurement bias (MLMB) in psychological assessment. Advances in Methods and Practices in Psychological Science, 5, 1-30.

Grants:
Algorithmic Racial Bias in Automated Video Interviews. SIOP Anti-racism grant (08/2020 – 07/2021). PI: L. Hickman. Co-PIs: L. Tay, S. E. Woo, & S. K. D’Mello.

Collaborative Research: AI-DCL EAGER: Understanding and Alleviating Potential Biases in Large Scale Employee Selection Systems: The Case of Automated Video Interviews. National Science Foundation. (8/15/2019 – 8/14/2021). Purdue PI: L. Tay; Co-I: S. E. Woo; University of Colorado Boulder PI: S. K. D’Mello.