Research

My research focuses on how technology influences the world of work and how we can use modern technologies, including machine learning (ML), artificial intelligence (AI), and natural language processing (NLP), to improve our understanding of human behavior. In doing so, my research also addresses social cognition and impression formation–or how people form judgments about the characteristics of others. My recent research has provided guidelines for using NLP in organizational research, developing and validated ML models for scoring interviews and assessment centers, and addressed concerns about bias and fairness in ML and AI. Today, my research continues to push the boundaries of assessment by drawing on new sources of data for evaluating individuals, developing methods for minimizing algorithmic bias, and advancing our understanding of interpersonal perception in modern workplaces. Learn more about my research in recent news articles and podcasts.

Awards and Achievements

 The interdisciplinary, award winning research that Louis conducts on organizational applications of artificial intelligence, machine learning, and natural language processing has appeared in Journal of Applied Psychology, Personnel Psychology, and Organizational Research Methods. Additionally, he has been a featured speaker for the National Association of Colleges and Employers (NACE), the International Convention of Psychological Science (ICPS), and the Consortium for the Advancement of Research Methods and Analysis (CARMA), and his research has been featured on the No Stupid Questions Podcast, WGN News, Times Higher Education, . 

 

Selected Publications

                                   My Research Gate

                               Google Scholar

Hickman, L., Kuruzovich, J., Ng, V., Arhin, K., & Wilson, D. (2023). Oversampling minority success during machine learning model training reduces adverse impact. In Zhang, N. Wang, M., Xu, H., Koenig, N., Hickman, L., Kuruzovich, J., Ng, V., Arhin, K., Wilson, D., Song, Q. C., Tang, C., Alexander, L., & Kim, Y. (2023). Reducing Subgroup Differences in Personnel Selection through the Application of Machine Learning. Personnel Psychology. https://doi.org/10.1111/peps.12593

Hickman, L., Herde, C. N., Lievens, F., & Tay, L. (2023). Automatic Scoring of Speeded Interpersonal Assessment Center Exercises Via Machine Learning: Initial Psychometric Evidence and Practical Guidelines. International Journal of Selection and Assessment, advance online publication. https://doi.org/10.1111/ijsa.12418

Lira, B., Gardner, M., Quirk, A., Stone, C., Rao, A., Ungar, L., Hutt, S., Hickman, L., D’Mello, S. K., & Duckworth, A. L. (2023). Using artificial intelligence to assess personal qualities in college admissions. Science Advances, 9(41), eadg9405.

Song, Q. C., Tang, C., Alexander III, L., Hickman, L., & Kim, Y. (2023). Multi-objective optimization for personnel selection: A guideline, tutorial, and user-friendly tool. In Zhang, N. Wang, M., Xu, H., Koenig, N., Hickman, L., Kuruzovich, J., Ng, V., Arhin, K., Wilson, D., Song, Q. C., Tang, C., Alexander, L., & Kim, Y. (2023). Reducing Subgroup Differences in Personnel Selection through the Application of Machine Learning. Personnel Psychology. https://doi.org/10.1111/peps.12593

Hickman, L., Bosch, N., Ng, V., Saef, R., Tay, L., & Woo, S. E. (2022). Automated video interview personality assessments: Reliability, validity, and generalizability investigations. Journal of Applied Psychology, 107(8), 1323–1351. https://doi.org/10.1037/apl0000695 *Winner of the 2024 Jeanneret Award for Excellence in the Study of Individual or Group
Assessment by the Society for Industrial and Organizational Psychology

Hickman, L., Thapa, S., Tay, L., Cao, M., & Srinivasan, P. (2022). Text preprocessing for text mining in organizational research: Review and recommendations. Organizational Research Methods, 25(1), 114-146. https://doi.org/10.1177/1094428120971683

Tay, L., Woo, S. E., Hickman, L., Booth, B. M., & D’Mello, S. (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), 1-30. https://doi.org/10.1177/25152459211061337

Van Tongeren, D. R., Ng, V., Hickman, L., & Tay, L. (2022). Behavioral measures of humility: Part 1. Theoretical and methodological review. The Journal of Positive Psychology, advace online publication, 1-11.

Van Tongeren, D. R., Ng, V., Hickman, L., & Tay, L. (2022). Behavioral measures of humility: Part 2. Conceptual mapping and charting ways forward. The Journal of Positive Psychology, advance online publication, 1-11.

Hickman, L., Saef, R., Ng, V., Tay, L., Woo, S. E., & Bosch, N. (2021). Developing and evaluating language-based machine learning algorithms for inferring applicant personality in video interviews. Human Resource Management Journal, advance online publication.https://doi.org/10.1111/1748-8583.12356

 Booth, B. M., Hickman, L., Subburaj, S. K., Rao, A., Tay, L., Woo, S. E., & D’Mello, S. K. (2021). Identifying and addressing latent bias in affective computing: Integrating approaches across disciplines and a case study of gender bias in automated video interview scoring. IEEE Signal Processing Magazine, 38(6), 84-95. https://doi.org/10.1109/MSP.2021.3106615

Booth, B. M., Hickman, L., Subburaj, S. K., Tay, L., Woo, S. E., & D’Mello, S. K. (2021). Bias and fairness in multimodal machine learning: A case study of automated video interviews. Proceedings of the 2021 International Conference on Multimodal Interaction (ICMI ’21). https://doi.org/10.1145/3462244.3479897

Saha, K., Yousuf, A., Hickman, L., Gupta, P., Tay, L.,& De Choudhury, M. (2021). A social media study on demographic differences in perceived job satisfaction. Proceedings of the ACM: Human Computer Interaction, 5(CSCW1), 1-29. https://doi.org/10.1145/3449241

Hickman, L., Tay, L., & Woo, S. E. (2019). Validity investigation of off-the-shelf language-based personality assessment using video interviews: Convergent and discriminant relationships with self and observer ratings. Personnel Assessment and Decisions, 5(3), 12-20. https://doi.org/10.25035/pad.2019.03.003

BOOK CHAPTERS

*Hickman, L., Song, Q. C., & Woo, S. E. (2022). Evaluating data. In K. Murphy (Ed.), Data, Methods, and Theory in Organizational Sciences. SIOP Organizational Frontiers Series.

*All authors contributed equally

Acheson, K., Finley, A., Hickman, L., Sternberger, L., & Shealy, C. (2020). Innovations in individual, interdisciplinary, and international assessment. In S. Nolan, C. Hakala, & R. E. Landrum (Eds.), Assessment: Individual, Institutional, and International Approaches. American Psychological Association.

Questions? Reach Out!