Daniel L. Chen, “Machine Learning and the Rule of Law”, 2019in Law as Data: Computation, Text, and the Future of Legal Analysis, Michael Livermore, and Daniel Rockmore (eds.), Santa Fe Institute Press, 2019.

Daniel L. Chen, Yosh Halberstam, Manoj Kumar, and Alan Yu, “Attorney Voice and the U.S. Supreme Court”, 2019in Law as Data: Computation, Text, and the Future of Legal Analysis, Michael Livermore, and Daniel Rockmore (eds.), Santa Fe Institute Press, 2019.

Daniel L. Chen, and Elliott Ash, “Case Vectors: Spatial Representations of the Law Using Document Embeddings”, Law as Data, vol. 11, 2019.

Daniel L. Chen, Charlotte Cavaillé, and Karine Van Der Straeten, “A Decision-Theoretic Approach to Understanding Survey Response: Likert vs. Quadratic Voting for Attitudinal Research”, The University of Chicago Law Review, vol. 22, n. Online, 2019.

Aurélie Ouss, and Arnaud Philippe, “No Hatred or Malice, Fear or Affection": Media and Sentencing”, Journal of Political Economy, vol. 126, n. 5, October 2018, pp. 2134–2178.

Thomas Christiano, “Fairness in Voluntary Transactions and the All Affected Principle”, in All Affected Principle, Dannielle Allen, Archon Fung, and Tomer Perry (eds.), Cambridge University Press, March 2018.

Daniel L. Chen, Adithya Parthasarathy, and Shivam Verma, “The Genealogy of Ideology: Identifying Persuasive Memes and Predicting Agreement in the U.S. Courts of Appeals”, in Proceedings of the ACM Conference on AI and the Law, 2018, forthcoming.

Daniel L. Chen, Matt Dunn, Levent Sagun, and Hale Sirin, “Early Predictability of Asylum Court Decisions”, in Proceedings of the ACM Conference on AI and the Law, 2018.

K.J. Martijn Cremers, and Simone M. Sepe, “Investors’ Time Preferences and Corporate Governance”, Seattle University Law Review, vol. 41, n. 2, 2018, pp. 387–418.

Daniel L. Chen, and Jess Eagel, “Can Machine Learning Help Predict the Outcome of Asylum Adjudications?”, in Proceedings of the ACM Conference on AI and the Law, 2018.