Journal Articles

  1. Krishnan, Ramayya, John P Lalor, Nicolas Prat, and Ahmed Abbasi. 2025. “From Policy to Practice: Research Directions for Trustworthy and Responsible AI ‘by Design’.” IEEE Intelligent Systems (Forthcoming).
  2. Li , Wenchang, John P Lalor, Yixing Chen, and Vamsi Kanuri. 2025. “From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision.” ACM Transactions on Management Information Systems 16 (3): 1–21.
  3. Yang, Yi, John P Lalor, Ahmed Abbasi, and Daniel Dajun Zeng. 2025. “Hierarchical Deep Document Model.” IEEE Transactions on Knowledge and Data Engineering 37 (1): 351–64. https://doi.org/10.1109/TKDE.2024.3487523.
  4. Lalor, John P, Ahmed Abbasi, Kezia Oketch , Yi Yang, and Nicole Forsgren. 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 42 (4): 99:1–41. https://doi.org/10.1145/3641276.
  5. Lalor, John P, David A Levy, Harmon S Jordan, Wen Hu, Jenni Kim Smirnova, and Hong Yu. 2024. “Evaluating Expert-Layperson Agreement in Identifying Jargon Terms in Electronic Health Record Notes: Observational Study.” Journal of Medical Internet Research 26: e49704. https://www.jmir.org/2024/1/e49704.
  6. Levy, David A, Harmon S Jordan, John P Lalor, et al. 2024. “Individual Factors That Affect Laypeople’s Understanding of Definitions of Medical Jargon.” Health Policy and Technology 13 (6): 100932. https://doi.org/10.1016/j.hlpt.2024.100932.
  7. Safadi, Hani, John P Lalor, and Nicholas Berente. 2024. “The Effect of Bots on Human Interaction in Online Communities.” MIS Quarterly 48 (3): 1279–95. https://aisel.aisnet.org/misq/vol48/iss3/15/.
  8. Lalor, John P, and Pedro Rodriguez. 2023. “py-irt: A Scalable Item Response Theory Library for Python.” INFORMS Journal on Computing 35 (1): 5–13. https://pubsonline.informs.org/doi/abs/10.1287/ijoc.2022.1250.
  9. Lalor, John P, Hao Wu, Kathleen M Mazor, and Hong Yu. 2023. “Evaluating the Efficacy of NoteAid on EHR Note Comprehension Among US Veterans Through Amazon Mechanical Turk.” International Journal of Medical Informatics 172: 105006. https://www.sciencedirect.com/science/article/abs/pii/S1386505623000230.
  10. Wowak, Kaitlin D, John P Lalor, Sriram Somanchi, and Corey M Angst. 2023. “Business Analytics in Healthcare: Past, Present, and Future Trends.” Manufacturing & Service Operations Management 25 (3): 975–95. https://doi.org/10.1287/msom.2023.1192.
  11. Lalor, John P, Wen Hu, Matthew Tran, Hao Wu, Kathleen M Mazor, and Hong Yu. 2021. “Evaluating the Effectiveness of NoteAid in a Community Hospital Setting: Randomized Trial of Electronic Health Record Note Comprehension Interventions With Patients.” Journal of Medical Internet Research 23 (5): e26354. https://www.jmir.org/2021/5/e26354/.
  12. Chen, Jinying, John P Lalor, Weisong Liu, et al. 2019. “Detecting Hypoglycemia Incidents Reported in Patients’ Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance.” Journal of Medical Internet Research 21 (3): e11990. https://doi.org/10.2196/11990.
  13. Lalor, John P, Beverly Woolf, and Hong Yu. 2019. “Improving Electronic Health Record Note Comprehension with Noteaid: Randomized Trial of Electronic Health Record Note Comprehension Interventions with Crowdsourced Workers.” Journal of Medical Internet Research 21 (1): e10793. https://www.jmir.org/2019/1/e10793/.
  14. Lalor, John P, Hao Wu, Li Chen, Kathleen M Mazor, and Hong Yu. 2018. “ComprehENotes, an Instrument to Assess Patient Reading Comprehension of Electronic Health Record Notes: Development and Validation.” Journal of Medical Internet Research 20 (4): e9380. https://www.jmir.org/2018/4/e139/.

Conference Proceedings

  1. Chen , Sihan, John P Lalor, Yi Yang, and Ahmed Abbasi. 2025. PersonaTwin: A Multi-Tier Prompt Conditioning Framework for Generating and Evaluating Personalized Digital Twins.
  2. Cook , Ryan, John P Lalor, and Ahmed Abbasi. 2025. “No Simple Answer to Data Complexity: An Examination of Instance-Level Complexity Metrics for Classification Tasks.” Proceedings of the 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics.
  3. Lalor, John P, Ruiyang Qin , David Dobolyi, and Ahmed Abbasi. 2025. “Textagon: Boosting Language Models with Theory-Guided Parallel Representations.” Proceedings of the 2025 Annual Meeting of the Association for Computational Linguistics.
  4. Oketch , Kezia, John P Lalor, and Ahmed Abbasi. 2025. “Cultural Artifacts, Tribal Heterogeneity, and Language Models.” International Conference on Information Systems (ICIS).
  5. Oketch , Kezia, John P Lalor, Yi Yang, and Ahmed Abbasi. 2025. “Bridging the LLM Accessibility Divide? Performance, Fairness, and Cost of Closed Versus Open Models for Automated Essay Scoring.” Proceedings of the GEM2 Workshop: Generation, Evaluation & Metrics - ACL 2025.
  6. Prat, Nicolas, John P Lalor, and Ahmed Abbasi. 2025. “GALEA – Leveraging Generative Agents in Artifact Evaluation.” Proceedings of the 20th International Conference on Design Science Research in Information Systems and Technology (DESRIST).
  7. Yang, Yi, Hanyu Duan , Ahmed Abbasi, John P Lalor, and Kar Yan Tam. 2025. “Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads.” Proceedings of the Fifth Workshop on Trustworthy Natural Language Processing (TrustNLP).
  8. Lalor, John P, Corey Angst, Fred Nwanganga, and John D’Arcy. 2024b. “It’s Not What You Say, It’s How You Say It: How Cultural Dimensions Impact GDPR Fine Summaries.” Twentieth symposium on statistical challenges in electronic commerce research.
  9. Lalor, John P, Corey Angst, Fred Nwanganga, and John D’Arcy. 2024a. “It’s Not What You Say, It’s How You Say It: How Cultural Dimensions Impact GDPR Fine Summaries.” Academy of management annual meeting.
  10. Lalor, John P, Pedro Rodriguez, João Sedoc, and Jose Hernandez-Orallo. 2024. “Item Response Theory for Natural Language Processing.” In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts, edited by Mohsen Mesgar and Sharid Loáiciga. Association for Computational Linguistics. https://aclanthology.org/2024.eacl-tutorials.2.
  11. Li , Wenchang, Yixing Chen, Shuang Zheng , Lei Wang, and John P Lalor. 2024. “Stars Are All You Need: A Distantly Supervised Pyramid Network for Unified Sentiment Analysis.” Proceedings of the Ninth Workshop on Noisy and User-Generated Text (w-NUT 2024), 104–18. https://aclanthology.org/2024.wnut-1.10/.
  12. Duan , Xiaojing, and John P Lalor. 2023. “H-COAL: Human Correction of AI-Generated Labels for Biomedical Named Entity Recognition.” Conference on Information Systems and Technology (CIST). https://arxiv.org/abs/2311.11981.
  13. Lalor, John P. 2023. “Ranking Pull Requests in Open Source Software.” Academy of management annual meeting.
  14. Lalor, John P. 2022. “On-the-Fly Difficulty Estimation for Deep Neural Networks.” INFORMS annual meeting.
  15. Lalor, John P, Yi Yang, Kendall Smith, Nicole Forsgren, and Ahmed Abbasi. 2022. “Benchmarking Intersectional Biases in NLP.” Proceedings of the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics.
  16. Rodriguez, Pedro, Phu Mon Htut, John P Lalor, and João Sedoc. 2022. “Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory.” Proceedings of the Third Workshop on Insights from Negative Results in NLP, 100–112. https://aclanthology.org/2022.insights-1.14/.
  17. Abbasi, Ahmed, David Dobolyi, John P Lalor, Richard G Netemeyer, Kendall Smith, and Yi Yang. 2021. “Constructing a Psychometric Testbed for Fair Natural Language Processing.” Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 3748–58. https://aclanthology.org/2021.emnlp-main.304/.
  18. Berente, Nicholas, John P Lalor, Sriram Somanchi, and Ahmed Abbasi. 2021. “The Illusion of Certainty and Data-Driven Decision Making in Emergent Situations.” International Conference on Information Systems (ICIS). https://aisel.aisnet.org/icis2021/gen_topics/gen_topics/10/.
  19. Lalor, John P, and Hong Guo. 2021. “Measuring Algorithmic Interpretability.” INFORMS annual meeting.
  20. Lalor, John P, Wen Hu, Matthew Tran, Kathleen Mazor, and Hong Yu. 2021. “Does Defining Medical Jargon In A Community Hospital Setting Improve Comprehension?” INFORMS healthcare conference.
  21. Rodriguez, Pedro, Joe Barrow, Alexander Miserlis Hoyle, John P Lalor, Robin Jia, and Jordan Boyd-Graber. 2021. “Evaluation Examples Are Not Equally Informative: How Should That Change NLP Leaderboards?” Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 4486–503. https://aclanthology.org/2021.acl-long.346/.
  22. Safadi, Hani, John P Lalor, and Nicholas Berente. 2021. “The Effect of Bots on Human Interaction in Online Communities.” International Conference on Information Systems (ICIS). https://aisel.aisnet.org/icis2021/ai_business/ai_business/1/.
  23. Lalor, John P, Nicholas Berente, and Hani Safadi. 2020. “Bots Versus Humans in Online Social Networks: A Study of Reddit Communities.” INSNA sunbelt conference.
  24. Lalor, John P, and Hong Guo. 2020. “Towards Measuring Algorithmic Interpretability.” INFORMS workshop on data science.
  25. Lalor, John P, and Hong Yu. 2020. “Dynamic Data Selection for Curriculum Learning via Ability Estimation.” Findings of the Association for Computational Linguistics: EMNLP 2020 2020: 545. https://aclanthology.org/2020.findings-emnlp.48/.
  26. Ma , Ming-Cheng, and John P Lalor. 2020. “An Empirical Analysis of Human-Bot Interaction on Reddit.” Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020) (Online), November, 101–6. https://doi.org/10.18653/v1/2020.wnut-1.14.
  27. Cho, Eunah, He Xie, John P Lalor, Varun Kumar, and William M Campbell. 2019. “Efficient Semi-Supervised Learning for Natural Language Understanding by Optimizing Diversity.” ASRU 2019: The IEEE automatic speech recognition and understanding workshop. https://arxiv.org/abs/1910.04196.
  28. Lalor, John P, Hao Wu, and Hong Yu. 2019a. “Comparing Human and DNN-Ensemble Response Patterns for Item Response Theory Model Fitting.” Workshop on cognitive modeling and computational linguistics (CMCL).
  29. Lalor, John P, Hao Wu, and Hong Yu. 2019b. “Learning Latent Parameters Without Human Response Patterns: Item Response Theory with Artificial Crowds.” Workshop on shortcomings in vision and language (SiVL).
  30. Lalor, John P, Hao Wu, and Hong Yu. 2019c. “Learning Latent Parameters Without Human Response Patterns: Item Response Theory with Artificial Crowds.” Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing 2019: 4240. https://aclanthology.org/D19-1434/.
  31. Chen, Jinying, John P Lalor, and Hong Yu. 2018. “Detecting Hypoglycemia Incidents from Patients’ Secure Messages.” American medical informatics association (AMIA) annual symposium.
  32. Lalor, John P, Hao Wu, Tsendsuren Munkhdalai, and Hong Yu. 2018. “Understanding Deep Learning Performance Through an Examination of Test Set Difficulty: A Psychometric Case Study.” Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing 2018: 4711. https://aclanthology.org/D18-1500/.
  33. Lalor, John P, Hao Wu, and Hong Yu. 2018a. “Modeling Difficulty to Understand Deep Learning Performance.” Northern lights deep learning workshop (NLDL).
  34. Lalor, John P, Hao Wu, and Hong Yu. 2018b. “Soft Label Memorization-Generalization for Natural Language Inference.” UAI workshop on uncertainty in deep learning. https://arxiv.org/abs/1702.08563v3.
  35. Lalor, John P, Hao Wu, Li Chen, Kathleen Mazor, and Hong Yu. 2017. “Generating a Test of Electronic Health Record Narrative Comprehension with Item Response Theory.” American medical informatics association (AMIA) annual symposium.
  36. Lalor, John P, Hao Wu, and Hong Yu. 2017. “CIFT: Crowd-Informed Fine-Tuning to Improve Machine Learning Ability.” Human Computation and Crowdsourcing (HCOMP). https://arxiv.org/abs/1702.08563v2.
  37. Lalor, John P, Hao Wu, and Hong Yu. 2016. “Building an Evaluation Scale Using Item Response Theory.” Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing 2016: 648. https://aclanthology.org/D16-1062/.
  38. Munkhdalai, Tsendsuren, John P Lalor, and Hong Yu. 2016. “Citation Analysis with Neural Attention Models.” Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis, 69–77. https://aclanthology.org/W16-6109/.
  39. Miller, Craig, Amber Settle, and John P Lalor. 2015. “Learning Object-Oriented Programming in Python: Towards an Inventory of Difficulties and Testing Pitfalls.” Proceedings of the 16th Annual Conference on Information Technology Education. https://dl.acm.org/doi/10.1145/2808006.2808017.
  40. Settle, Amber, John P Lalor, and Theresa Steinbach. 2015a. “A Computer Science Linked-Courses Learning Community.” Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education, 123–28. https://dl.acm.org/doi/10.1145/2729094.2742621.
  41. Settle, Amber, John P Lalor, and Theresa Steinbach. 2015b. “Evaluating a Linked-Courses Learning Community for Development Majors.” Proceedings of the 16th Annual Conference on Information Technology Education, 127–32. https://dl.acm.org/doi/10.1145/2808006.2808031.
  42. Settle, Amber, John P Lalor, and Theresa Steinbach. 2015c. “Reconsidering the Impact of CS1 on Novice Attitudes.” Proceedings of the 46th ACM Technical Symposium on Computer Science Education, 229–34. https://dl.acm.org/doi/10.1145/2676723.2677235.

Under Review/Revision

  1. Costello, John, Yixing Chen, John P Lalor, and Robert Guo. Rate Before You Write: How the Presence and Positioning of Multidimensional Attribute Ratings Influence Attrition in Online Reviews.
  2. Lalor, John P, Vamsi Kanuri, and Ishita Chakraborty. FEWD: A Fused Explainable Model Using Wide and Deep Networks for Synthesizing Multi-Modal Content.
  3. Li , Shaochun, Ahmed Abbasi, Faizan Ahmad, John P Lalor, and Nitesh Chawla. Modeling Edge-Rich Graphs Using Neural Networks.
  4. Meng , Guangyu, Qingkai Zeng , John P Lalor, and Hong Yu. A Psychology-Based Unified Dynamic Framework for Curriculum Learning. https://arxiv.org/abs/2408.05326.
  5. Mohlmann, Mareike, John P Lalor, Yoon Son, and Nicholas Berente. Inflation in Reputation Systems? Newcomers, Veterans, and Socialization into a Platform Context.
  6. Zheng , Shuang, John P Lalor, and Yixing Chen. Diversifying Recommendations on Digital Platforms: A Dynamic Graph Neural Network Approach.

Working Papers

  1. Cook , Ryan, John P Lalor, and Ahmed Abbasi. 2025. CADE: Classification with Automatic Difficulty Estimation.
  2. Oketch , Kezia, John P Lalor, and Ahmed Abbasi. 2025. Is Linguistic Variation Signal or Noise? A Taxonomy-Guided Evaluation of Sociolinguistic Diversity in Swahili NLP.
  3. Lim , Jung Hoon, Sunjae Kwon , Zonghai Yao , John P Lalor, and Hong Yu. 2024. “Large Language Model-Based Role-Playing for Personalized Medical Jargon Extraction.” https://arxiv.org/abs/2408.05555.
  4. Lalor, John P, and Rene Just. 2023. Ranking Pull Requests in Open Source Software.
  5. Lalor, John P, Corey Angst, Sriram Somanchi, John D’Arcy, and Fred Nwanganga. When Uniform Regulation Meets Local Realities: A Theory of Distributed Decoupling in the Case of GDPR and Empirical Validation.
  6. Lalor, John P, Hong Guo, Nicholas Berente, Ahmed Abbasi, and Jan Recker. Measuring Algorithmic Interpretability: A Human-Learning-Based Framework and the Corresponding Cognitive Complexity Score.
  7. Lalor, John P, and Shawn Qu. On the Production and Spread of News in a Digital Age.
  8. Zhao, Zifeng, Shawn Qu, John P Lalor, and Ahmed Abbasi. Learning from the Curve: Predicting Successful Projects Using Functional PCA.