Editorial: Statistical Aspects of Trustworthy Machine Learning
Volume 24, Issue 1 (2026): Special Issue: Statistical aspects of Trustworthy Machine Learning, pp. 1–3
Pub. online: 11 February 2026
Type: Editorial
Open Access
Open Access
Published
11 February 2026
11 February 2026
References
D’Agostino McGowan L, Peng RD, Hicks SC (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
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 JD, 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
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
Liu LYf, 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
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
Song Z, Cai T, Lee JD, Su WJ (2026). Reward collapse in aligning large language models. Journal of Data Science, 24(1): 146–166. https://doi.org/10.6339/25-JDS1201
Uddin MB, 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
Wang L, Richardson T, Robins J (2026a). Causal inference: A tale of three frameworks. Journal of Data Science, 24(1): 53–85. https://doi.org/10.6339/25-JDS1211
Wang S, Xu L, Liu J, Zhai Y (2026b). 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
Yu P, Jiang Y, Su Z, Wu J, Kang 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
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
Zhou Y (2026). Reinforcement learning: A statistical perspective. Journal of Data Science, 24(1): 86–105. https://doi.org/10.6339/25-JDS1205
