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:
Xu, G., Yu, H., Wei, L., Liu, Y., Liu, D., Xu, C., Li, J., Abbasi, A., Xiong, J., Yu, X., Zheng, Z., Shi, Y., & Qin, R. Assessing the use of automatic speech recognition and large language models for individuals with language impairments. Nature Communications (2026).
Zhang, D., Wang, R., Liu, J., Oketch, K., Shi, Y., Ji, H., and Abbasi, A. (2026). Pavlov's Dog and Large Language Models: The Double-Edged Power of Context Conditioning. IEEE Intelligent Systems, forthcoming.
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
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