| 2025 | ACL | Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation. | Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu |
| 2024 | CogSci | Dynamics of Causal Attribution. | Dana Kulzhabayeva, Joseph Jay Williams, David Danks |
| 2024 | QCE | Identification and Mitigating Bias in Quantum Machine Learning. | Nandhini Swaminathan, David Danks |
| 2023 | CogSci | Expectations of Determinism Underlie Domain Effects on Adult Causal Learning. | Phuong (Phoebe) Dinh, David Danks |
| 2023 | ICLR | GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints. | Mohammadsajad Abavisani, David Danks, Sergey M. Plis |
| 2022 | CogSci | Expectations of Causal Determinism in Causal Learning. | Phuong (Phoebe) Dinh, David Danks |
| 2022 | CogSci | Homophily and Incentive Effects in Use of Algorithms. | Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta, Zachary C. Lipton, David Danks |
| 2021 | AIES | Ethical Obligations to Provide Novelty. | Paige Golden, David Danks |
| 2021 | CogSci | Reason-Based Constraint in Theory of Mind. | Corey J. Cusimano, Natalia C. Zorrilla, David Danks, Tania Lombrozo |
| 2021 | CogSci | Individual Differences in Causal Learning. | Laila Johnston, Noah Hillman, David Danks |
| 2020 | AIES | Good Explanation for Algorithmic Transparency. | Joy Lu, Dokyun Lee, Tae Wan Kim, David Danks |
| 2020 | AIES | Different "Intelligibility" for Different Folks. | Yishan Zhou, David Danks |
| 2020 | CogSci | Effects of Causal Determinism on Causal Learning Trajectories. | Phuong (Phoebe) Dinh, David Danks |
| 2019 | AIES | The Value of Trustworthy AI. | David Danks |
| 2019 | AIES | Balancing the Benefits of Autonomous Vehicles. | Timothy Geary, David Danks |
| 2019 | AIES | How Technological Advances Can Reveal Rights. | Jack Parker, David Danks |
| 2018 | AIES | Impacts on Trust of Healthcare AI. | Emily LaRosa, David Danks |
| 2018 | AIES | Regulating Autonomous Vehicles: A Policy Proposal. | Alex John London, David Danks |
| 2018 | CogSci | Generalizations, from representation to transmission. | Michael Henry Tessler, Noah D. Goodman, David Danks, Emily Foster-Hanson, Marjorie Rhodes, Greg Carlson |
| 2017 | IJCAI | Algorithmic Bias in Autonomous Systems. | David Danks, Alex John London |
| 2017 | ICSE | Causal modeling, discovery & inference for software engineering. | Rick Kazman, Robert Stoddard, David Danks, Yuanfang Cai |
| 2015 | UAI | Mesochronal Structure Learning. | Sergey M. Plis, David Danks, Jianyu Yang |
| 2014 | CogSci | Learning with a Purpose: The Influence of Goals. | Sarah Wellen, David Danks |
| 2013 | CogSci | What if? Counterfactual reasoning, pretense, and the role of possible worlds. | Daphna Buchsbaum, Caren M. Walker, Alison Gopnik, Nick Chater, David Danks, Christopher G. Lucas, Charles Kemp, Eva Rafetseder, Josef Perner |
| 2013 | CogSci | Moving from Levels & Reduction to Dimensions & Constraints. | David Danks |
| 2012 | CogSci | Actor-Observer Asymmetries in Judgments of Intentional Actions. | Sarah Wellen, David Danks |
| 2012 | CogSci | Learning Causal Structure through Local Prediction-error Learning. | Sarah Wellen, David Danks |
| 2002 | DIS | Learning the Causal Structure of Overlapping Variable Sets. | David Danks |
| 2001 | UAI | Linearity Properties of Bayes Nets with Binary Variables. | David Danks, Clark Glymour |