Daniel L. Chen

Elliott Ash, and Daniel L. Chen, “Judge Embeddings: Vector Representations of Legal Belief”, Computational Analysis of Law, 2019, forthcoming.

Daniel L. Chen, “Intermediated Social Preferences: Altruism in an Algorithmic Era”, in Advances in Economics of Religion, Jean-Paul Carvalho, Sriya Iyer, and Jared Rubin (eds.), Palgrave Macmillan, series “International Economic Association Series”, vol. 158, 2019, forthcoming.

Daniel L. Chen, Xing Cui, Lanyu Shang, and Junchao Zheng, “What Matters: Agreement Between U.S. Courts of Appeals Judges”, Journal of Machine Learning Research, 2019, forthcoming.

Charlotte Cavaillé, Daniel L. Chen, and Karine Van Der Straeten, “Towards a General Theory of Survey Response: Likert Scales Vs. Quadratic Voting for Attitudinal Research”, The University of Chicago Law Review, 2019, forthcoming.

Daniel L. Chen, “Judicial Analytics and the Great Transformation of American Law”, Artificial Intelligence and the Law, 2019, forthcoming.

Daniel L. Chen, “Tastes for Desert and Placation: A Reference Point-Dependent Model of Social Preferences”, Research in Experimental Economics, 2019, forthcoming.

Daniel L. Chen, “Law and Literature: Theory and Evidence on Empathy and Guile”, Review of Law and Economics, 2019.

Daniel L. Chen, “Case Vectors: Spatial Representations of the Law Using Document Embeddings”, Computational Analysis of Law, 2019, forthcoming.

Daniel L. Chen, “Machine Learning and Rule of Law”, Computational Analysis of Law, 2019, forthcoming.

Daniel L. Chen, “Attorney Voice and the U.S. Supreme Court”, Computational Analysis of Law, 2019, forthcoming.

Daniel L. Chen, Manoj Kumar, Vishal Motwani, and Philip Yeres, “Is Justice Really Blind? And Is It Also Deaf?”, Computational Analysis of Law, 2019, forthcoming.

Elliott Ash, and Daniel L. Chen, “What Kind of Judge is Brett Kavanaugh?: A Quantitative Analysis”, Cardozo Law Review, 2018, pp. 70–100.

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.

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.

Elliott Ash, Daniel L. Chen, Nischal Mainali, and Liam Meier, “Automated Classification of Modes of Moral Reasoning in Judicial Decisions”, Computational Legal Studies, 2018, forthcoming.

Adam B. Badawi, and Daniel L. Chen, “The Shareholder Wealth Effects of Delaware Litigation”, American Law and Economics Review, vol. 19, n. 2, October 2017, pp. 287–326.

Carlos Berdejo, and Daniel L. Chen, “Electoral Cycles Among U.S. Courts of Appeals Judges”, The Journal of Law and Economics, vol. 60, n. 3, August 2017, pp. 479–496.

Daniel L. Chen, Tobias J. Moskowitz, and Kelly Shue, “Decision-Making under the Gambler's Fallacy: Evidence from Asylum Judges, Loan Officers, and Baseball Umpires”, The Quarterly Journal of Economics, vol. 131, n. 3, August 2016, pp. 1181–1241.

Daniel L. Chen, and John J. Horton, “Are Online Labor Markets Spot Markets for Tasks? A Field Experiment on the Behavioral Response to Wages Cuts”, Information Systems Research, vol. 27, n. 2, May 2016, pp. 403–423.