Research and Development

Machine Learning and Decision-making


Predicting Human Decisions with Behavioral Theories and Machine Learning by Ori Plonsky, Reut Apel, Eyal Ert, Moshe Tennenholtz, David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Thomas L. Griffiths, Stuart J. Russell, Evan C. Carter, James F. Cavanagh and Idoa Erev, Combining Psychological Models with Machine Learning to Better Predict People’s Decisions by Avi Rosenfeld, Inon Zuckerman, Amos Azaria and Sarit Kraus, A Personalized Computational Model for Human-like Automated Decision-making by Longsheng Jiang and Yue Wang, Behavior-based Machine-learning: A Hybrid Approach for Predicting Human Decision Making by Gali Noti, Effi Levi, Yoav Kolumbus and Amit Daniely, Psychological Forest: Predicting Human Behavior by Ori Plonsky, Ido Erev, Tamir Hazan and Moshe Tennenholtz, Predicting Human Decision-making: From Prediction to Action by Ariel Rosenfeld and Sarit Kraus, Cognitive Model Priors for Predicting Human Decisions by David D. Bourgin, Joshua C. Peterson, Daniel Reichman, Stuart J. Russell and Thomas L. Griffiths, Advances in Machine Learning for the Behavioral Sciences by Tomáš Kliegr, Štěpán Bahník and Johannes Fürnkranz, Using Large-scale Experiments and Machine Learning to Discover Theories of Human Decision-making by Joshua C. Peterson, David D. Bourgin, Mayank Agrawal, Daniel Reichman and Thomas L. Griffiths and A Neural Network Walks into a Lab: Towards Using Deep Nets as Models for Human Behavior by Wei Ji Ma and Benjamin Peters.


Rationale Discovery and Explainable AI by Erich Schweighofer and Discovering the Rationale of Decisions: Towards a Method for Aligning Learning and Reasoning by Cor Steging, Silja Renooij and Bart Verheij.


A Computational Model of Commonsense Moral Decision Making by Richard Kim, Max Kleiman-Weiner, Andrés Abeliuk, Edmond Awad, Sohan Dsouza, Joshua B. Tenenbaum and Iyad Rahwan, Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning by Mayank Agrawal, Joshua C. Peterson and Thomas L. Griffiths, Modeling of Moral Decisions with Deep Learning by Christopher Wiedeman, Ge Wang and Uwe Kruger, Grounding Value Alignment with Ethical Principles by Tae Wan Kim, Thomas Donaldson and John Hooker, The Alignment Problem: Machine Learning and Human Values by Brian Christian and Artificial Intelligence, Values, and Alignment by Iason Gabriel.


The Debate Judge as a Machine by Dale D. Drum, Computational Argumentation Quality Assessment in Natural Language by Henning Wachsmuth, Nona Naderi, Yufang Hou, Yonatan Bilu, Vinodkumar Prabhakaran, Tim Alberdingk Thijm, Graeme Hirst and Benno Stein, Argumentation Quality Assessment: Theory vs. Practice by Henning Wachsmuth, Nona Naderi, Ivan Habernal, Yufang Hou, Graeme Hirst, Iryna Gurevych and Benno Stein, A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis by Shai Gretz, Roni Friedman, Edo Cohen-Karlik, Assaf Toledo, Dan Lahav, Ranit Aharonov and Noam Slonim, Towards Automated Analysis of Student Arguments by Nancy L. Green, Modeling Argument Strength in Student Essays by Isaac Persing and Vincent Ng, Towards Debate Automation: A Recurrent Model for Predicting Debate Winners by Peter Potash and Anna Rumshisky, A Multimodal Predictive Model of Successful Debaters or How I Learned to Sway Votes by Maarten Brilman and Stefan Scherer, Multimodal Prediction of the Audience's Impression in Political Debates by Pedro B. Santos and Iryna Gurevych and Winning on the Merits: The Joint Effects of Content and Style on Debate Outcomes by Lu Wang, Nick Beauchamp, Sarah Shugars and Kechen Qin.


Argumentation in the Framework of Deliberation Dialogue by Douglas Walton, Katie Atkinson, Trevor Bench-Capon, Adam Wyner and Dan Cartwright, Debate in a Multi-agent System: Multiparty Argumentation Protocols by Dionysios Kontarinis, A Multi-agent Argumentation Framework to Support Collective Reasoning by Jordi Ganzer-Ripoll, Maite López-Sánchez and Juan A. Rodriguez-Aguilar, Empirical Evaluation of Strategies for Multiparty Argumentative Debates by Dionysios Kontarinis, Elise Bonzon, Nicolas Maudet and Pavlos Moraitis, Methods for Analyzing and Measuring Group Deliberation by Laura W. Black and Stephanie Burkhalter, Modeling and Measuring Deliberation Online by Nick Beauchamp, Measuring Deliberation's Content: A Coding Scheme by Jennifer Stromer-Galley, Deliberative Systems: Deliberative Democracy at the Large Scale edited by John Parkinson and Jane Mansbridge and Mapping and Measuring Deliberation: Towards a New Deliberative Quality by André Bächtiger and John Parkinson.


Machine Learning and Legal Argument by Jack Mumford and Katie Atkinson, Cases and Stories, Dimensions and Scripts by Trevor J. M. Bench-Capon and Floris Bex, A Brief History of the Changing Roles of Case Prediction in AI and Law by Kevin D. Ashley, Algorithmic Decision-making and the Law by Dirk Brand, On the Relevance of Algorithmic Decision Predictors for Judicial Decision Making by Floris Bex and Henry Prakken, Judge v Robot?: Artificial Intelligence and Judicial Decision-making by Tania Sourdin, Machine Learning, Text Data, and Supreme Court Forecasting by Aaron Kaufman, Peter Kraft and Maya Sen, A Predictive Performance Comparison of Machine Learning Models for Judicial Cases by Zhenyu Liu and Huanhuan Chen, Using Machine Learning to Predict Judicial Decisions by Conor O'Sullivan, Rationale Discovery and Explainable AI by Erich Schweighofer and Discovering the Rationale of Decisions: Towards a Method for Aligning Learning and Reasoning by Cor Steging, Silja Renooij and Bart Verheij.


Towards Principles of Good Digital Administration: Fairness, Accountability and Proportionality in Automated Decision-making by Arjan Widlak, Marlies van Eck and Rik Peeters, Responsible and Accountable Algorithmization: How to Generate Citizen Trust in Governmental Usage of Algorithms by Albert Meijer and Stephan Grimmelikhuijsen and Between Technochauvinism and Human-centrism: Can Algorithms Improve Decision-making in Democratic Politics? by Pascal D. König and Georg Wenzelburger.