Psychometric NLP & AI Governance

Research on psychometric NLP, fairness, AI governance includes novel machine learning methods for text classification, user-centric language modeling, and fairness in NLP. We explore ways to better understand the human condition through machine learning, with implications for downstream policies, interventions, decision-making, and AI governance. Example research:

• Lalor, J. P., Abbasi, A., Oketch, K., Yang, Y., & Forsgren, N. (2024). Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning Pipelines. ACM Transactions on Information Systems, forthcoming

• Yang, K., Lau, Raymond Y. K., & Abbasi, A. (2023). Getting Personal: A Deep Learning Artifact for Text-Based Measurement of Personality. Information Systems Research, 34(1), pp.194-222

• Guo, Y., Yang, Y., & Abbasi, A. (2022).Auto-debias: Debiasing Masked Language Models with Automated Biased Prompts. Association for Computational Linguistics, May 22-27, 1012-1023.

• Mao, W., Qiu, X., & Abbasi, A. (2024).LLMs and their Applications in Medical Artificial Intelligence. ACM Transactions on MIS, forthcoming.

• Ahmad, F.,Abbasi, A., Li, J., Dobolyi, D., Netemeyer, R., Clifford, G., & Chen, H. (2020). A Deep Learning Architecture for Psychometric Natural Language Processing. ACM Transactions on Information Systems, 38(1), no. 6.

• Lalor, J. P., Wu, H., Munkhdalai, T., & Yu, H. (2018). Understanding Deep Learning Performance Through an Examination of Test Set Difficulty: A Psychometric Case Study. Empirical Methods in Natural Language Processing, Oct 31 – Nov 4, 4711-4716.

• Abbasi, A., Li, J., Clifford, G. D., & Taylor, H. A. (2018). Make ‘Fairness by Design’ Part of Machine Learning. Harvard Business Review, August 5