ASA Connect

We are pleased to release the Journal of Data Science (https://jds-online.org) special issue "Statistical Aspects of Trustworthy Machine Learning" (Vol. 24, Issue 1, January 2026), inspired by the 2024 Banff International Research Station workshop on the same theme. This issue brings together contributions on interpretability/explainability, fairness, robustness, privacy, transparency, and uncertainty, emphasizing the central role of statistical thinking in advancing trustworthy ML. It also features a focused discussion on the role of AI in data science education, including perspectives on AI-generated coursework and implications for pedagogy and assessment. We are deeply grateful to the authors, reviewers, and discussants for their thoughtful contributions and to the workshop participants whose discussions helped shape this collection.

Jun Yan, on behalf of the editorial team

Stephanie Hicks (Johns Hopkins University)
Keegan Korthauer (University of British Columbia)

Xiaotong Shen (University of Minnesota)
Jun Yan (University of Connecticut)
Hao Helen Zhang (University of Arizona)

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# Journal of Data Science, Volume 24, Issue 1, 2026

Hicks, S., Korthauer, K., Shen, X., Yan, J., & Zhang, H. (2026). Editorial: Statistical Aspects of Trustworthy Machine Learning. Journal of Data Science, 24(1), 1-3. https://doi.org/10.6339/26-JDS241EDI

Jiang, Y., Zhang, Z., Martin, R., & Liu, C. (2026). The Typicality Principle and Its Implications for Statistics and Data Science. Journal of Data Science, 24(1), 4-25. https://doi.org/10.6339/26-JDS1217

Sankaran, K. (2026). Data Science Principles for Interpretable and Explainable AI. Journal of Data Science, 24(1), 26-52. https://doi.org/10.6339/24-JDS1150

Wang, L., Richardson, T. S., & Robins, J. M. (2026). Causal Inference: A Tale of Three Frameworks. Journal of Data Science, 24(1), 53-85. https://doi.org/10.6339/25-JDS1211

Zhou, Y. (2026). Reinforcement Learning: A Statistical Perspective. Journal of Data Science, 24(1), 86-105. https://doi.org/10.6339/25-JDS1205

Fu, V. (2026). AI for Science: Opportunities, Challenges, and Future Directions. Journal of Data Science, 24(1), 106-124. https://doi.org/10.6339/25-JDS1214

Goode, K., Tucker, J. D., Ries, D., & Hofmann, H. (2026). Explainable Machine Learning for Functional Data. Journal of Data Science, 24(1), 125-145. https://doi.org/10.6339/25-JDS1212

Song, Z., Cai, T., Lee, J. D., & Su, W. J. (2026). Reward Collapse in Aligning Large Language Models. Journal of Data Science, 24(1), 146-166. https://doi.org/10.6339/25-JDS1201

Liu, L. Y., Ma, H., Liu, Y., & Zhu, H. (2026). Subject-Specific Scalar-on-Image Regression. Journal of Data Science, 24(1), 167-186. https://doi.org/10.6339/25-JDS1203

Yu, P., Jiang, Y., Su, Z., Wu, J., Kong, L., & Jiang, B. (2026). Differentially Private Bayesian Envelope Regression via Sufficient Statistic Perturbation. Journal of Data Science, 24(1), 187-202. https://doi.org/10.6339/25-JDS1194

Uddin, M. B., Yin, M., & Dasgupta, N. (2026). A Designed Look at Artificial Intelligence from the Lens of Fairness. Journal of Data Science, 24(1), 203-217. https://doi.org/10.6339/26-JDS1219

Zhang, M., Sun, Y., & Liang, F. (2026). Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior. Journal of Data Science, 24(1), 218-238. https://doi.org/10.6339/24-JDS1156

D'Agostino McGowan, L., Peng, R. D., & Hicks, S. C. (2026). Quantifying the Alignment of a Data Analysis Between Analyst and Audience. Journal of Data Science, 24(1), 239-253. https://doi.org/10.6339/25-JDS1189

Wang, S., Xu, L., Liu, J., & Zhai, Y. (2026). Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies. Journal of Data Science, 24(1), 254-260. https://doi.org/10.6339/25-JDS1208

Chan, S. L., & Pua, A. A. Y. (2026). Discussion of "Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies". Journal of Data Science, 24(1), 261-263. https://doi.org/10.6339/25-JDS1208C

Columbus, A. (2026). Discussion of "Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies". Journal of Data Science, 24(1), 264-266. https://doi.org/10.6339/25-JDS1208D

Furfaro, E. (2026). Discussion of "Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies". Journal of Data Science, 24(1), 267-268. https://doi.org/10.6339/25-JDS1208B

Huo, X., & Ni, X. S. (2026). Discussion of "Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies". Journal of Data Science, 24(1), 269-271. https://doi.org/10.6339/25-JDS1208F

Lock, P. F. (2026). Discussion of "Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies". Journal of Data Science, 24(1), 272-273. https://doi.org/10.6339/25-JDS1208G

Loux, T. (2026). Discussion of "Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies". Journal of Data Science, 24(1), 274-275. https://doi.org/10.6339/25-JDS1208E

Nicosia, A. (2026). Discussion of "Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies". Journal of Data Science, 24(1), 276-281. https://doi.org/10.6339/26-JDS1208H

Zaghi, A., & Harel, O. (2026). Discussion of "Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies". Journal of Data Science, 24(1), 282-285. https://doi.org/10.6339/25-JDS1208A

Wang, S., Xu, L., Liu, J., & Zhai, Y. (2026). Rejoinder: "Addressing the Challenges of AI-Generated Assignment Submissions in Education: Insights and Strategies". Journal of Data Science, 24(1), 286-291. https://doi.org/10.6339/26-JDS1208I