Linearity Properties of Bayes Nets with Binary Variables. - David Danks; Clark Glymour (UAI, 2001)

Learning the Causal Structure of Overlapping Variable Sets. - David Danks (Discovery Science, 2002)

Dynamical Causal Learning. - David Danks; Thomas L. Griffiths 001; Joshua B. Tenenbaum (NIPS, 2002)

Constraint-Based Human Causal Learning. - David Danks (ICCM, 2004)

Integrating Locally Learned Causal Structures with Overlapping Variables. - Robert E. Tillman; David Danks; Clark Glymour (NIPS, 2008)

Actual causation: a stone soup essay. - Clark Glymour; David Danks; Bruce Glymour; Frederick Eberhardt; Joseph D. Ramsey; Richard Scheines; Peter Spirtes; Choh Man Teng 001; Jiji Zhang (Synth., 2010)

Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. - Frederick Eberhardt; David Danks (Minds Mach., 2011)

Actor-Observer Asymmetries in Judgments of Intentional Actions. - Sarah Wellen; David Danks (CogSci, 2012)

Learning Causal Structure through Local Prediction-error Learning. - Sarah Wellen; David Danks (CogSci, 2012)

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 (CogSci, 2013)

Moving from Levels & Reduction to Dimensions & Constraints. - David Danks (CogSci, 2013)

Tracking Time-varying Graphical Structure. - Erich Kummerfeld; David Danks (NIPS, 2013)

Wisdom of crowds versus groupthink: learning in groups and in isolation. - Conor Mayo-Wilson; Kevin J. S. Zollman; David Danks (Int. J. Game Theory, 2013)

Learning with a Purpose: The Influence of Goals. - Sarah Wellen; David Danks (CogSci, 2014)

Model change and reliability in scientific inference. - Erich Kummerfeld; David Danks (Synth., 2014)

Erratum to: Model change and methodological virtues in scientific inference. - Erich Kummerfeld; David Danks (Synth., 2014)

Rate-Agnostic (Causal) Structure Learning. - Sergey M. Plis; David Danks; Cynthia Freeman; Vince D. Calhoun (NIPS, 2015)

Mesochronal Structure Learning. - Sergey M. Plis; David Danks; Jianyu Yang (UAI, 2015)

Goal-dependence in (scientific) ontology. - David Danks (Synth., 2015)

Causal Discovery from Subsampled Time Series Data by Constraint Optimization. - Antti Hyttinen; Sergey M. Plis; Matti Järvisalo; Frederick Eberhardt; David Danks (Probabilistic Graphical Models, 2016)

Adaptively Rational Learning. - Sarah Wellen; David Danks (Minds Mach., 2016)

Causal modeling, discovery & inference for software engineering. - Rick Kazman; Robert Stoddard; David Danks; Yuanfang Cai (ICSE (Companion Volume), 2017)

Algorithmic Bias in Autonomous Systems. - David Danks; Alex John London (IJCAI, 2017)

Regulating Autonomous Systems: Beyond Standards. - David Danks; Alex John London (IEEE Intell. Syst., 2017)

A constraint optimization approach to causal discovery from subsampled time series data. - Antti Hyttinen; Sergey M. Plis; Matti Järvisalo; Frederick Eberhardt; David Danks (Int. J. Approx. Reason., 2017)

Impacts on Trust of Healthcare AI. - Emily LaRosa; David Danks (AIES, 2018)

Regulating Autonomous Vehicles: A Policy Proposal. - Alex John London; David Danks (AIES, 2018)

Generalizations, from representation to transmission. - Michael Henry Tessler; Noah D. Goodman; David Danks; Emily Foster-Hanson; Marjorie Rhodes; Greg Carlson (CogSci, 2018)

The Value of Trustworthy AI. - David Danks (AIES, 2019)

Balancing the Benefits of Autonomous Vehicles. - Timothy Geary; David Danks (AIES, 2019)

How Technological Advances Can Reveal Rights. - Jack Parker; David Danks (AIES, 2019)

Amalgamating evidence of dynamics. - David Danks; Sergey M. Plis (Synth., 2019)

Good Explanation for Algorithmic Transparency. - Joy Lu; Dokyun Lee; Tae Wan Kim; David Danks (AIES, 2020)

Different "Intelligibility" for Different Folks. - Yishan Zhou; David Danks (AIES, 2020)

Effects of Causal Determinism on Causal Learning Trajectories. - Phuong (Phoebe) Dinh; David Danks (CogSci, 2020)

Ethical Obligations to Provide Novelty. - Paige Golden; David Danks (AIES, 2021)

Reason-Based Constraint in Theory of Mind. - Corey J. Cusimano; Natalia C. Zorrilla; David Danks; Tania Lombrozo (CogSci, 2021)

Individual Differences in Causal Learning. - Laila Johnston; Noah Hillman; David Danks (CogSci, 2021)

AI4People: Ethical Guidelines for the Automotive Sector - Fundamental Requirements and Practical Recommendations. - Christoph Lütge; Franziska Poszler; Aida Joaquin Acosta; David Danks; Gail Gottehrer; Lucian Mihet-Popa; Aisha Naseer (Int. J. Technoethics, 2021)

The case for information fiduciaries: The implementation of a data ethics checklist at Seattle Children's Hospital. - Elizabeth Montague; T. Eugene Day; Dwight Barry; Maria Brumm; Aaron McAdie; Andrew B. Cooper; Julia Wignall; Steve Erdman; Diahnna Núñez; Douglas Diekema; David Danks (J. Am. Medical Informatics Assoc., 2021)

Governing AI safety through independent audits. - Gregory Falco; Ben Shneiderman; Julia Badger; Ryan Carrier; Anton Dahbura; David Danks; Martin Eling; Alwyn Goodloe; Jerry Gupta; Christopher Hart; Marina Jirotka; Henric Johnson; et al. (Nat. Mach. Intell., 2021)

Expectations of Causal Determinism in Causal Learning. - Phuong (Phoebe) Dinh; David Danks (CogSci, 2022)

Homophily and Incentive Effects in Use of Algorithms. - Riccardo Fogliato; Sina Fazelpour; Shantanu Gupta; Zachary C. Lipton; David Danks (CogSci, 2022)

Constraint-Based Causal Structure Learning from Undersampled Graphs. - Mohammadsajad Abavisani; David Danks; Sergey M. Plis (CoRR, 2022)

The Challenge of Ethical Interoperability. - David Danks; Daniel Trusilo (Digit. Soc., 2022)

Causal Learning through Deliberate Undersampling. - Kseniya Solovyeva; David Danks; Mohammadsajad Abavisani; Sergey M. Plis (CLeaR, 2023)

Expectations of Determinism Underlie Domain Effects on Adult Causal Learning. - Phuong (Phoebe) Dinh; David Danks (CogSci, 2023)

GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints. - Mohammadsajad Abavisani; David Danks; Sergey M. Plis (ICLR, 2023)

The Ambiguity of Robot Rights. - Anisha Bontula; David Danks; Naomi T. Fitter (ICSR (1), 2023)

Fairness Vs. Personalization: Towards Equity in Epistemic Utility. - Jennifer Chien; David Danks (CoRR, 2023)

Artificial intelligence and humanitarian obligations. - Daniel Trusilo; David Danks (Ethics Inf. Technol., 2023)

Ethical and methodological challenges in building morally informed AI systems. - Thilo Hagendorff; David Danks (AI Ethics, 2023)

Dynamic Certification for Autonomous Systems. - Georgios Bakirtzis; Steven Carr 002; David Danks; Ufuk Topcu (Commun. ACM, 2023)

Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation. - Jennifer Chien; David Danks (FAccT, 2024)

Identification and Mitigating Bias in Quantum Machine Learning. - Nandhini Swaminathan; David Danks (QCE, 2024)

Commercial AI, Conflict, and Moral Responsibility: A theoretical analysis and practical approach to the moral responsibilities associated with dual-use AI technology. - Daniel Trusilo; David Danks (CoRR, 2024)

Future of Pandemic Prevention and Response CCC Workshop Report. - David Danks; Rada Mihalcea; Katie A. Siek; Mona Singh 001; Brian Dixon; Haley Griffin (CoRR, 2024)

Application of the NIST AI Risk Management Framework to Surveillance Technology. - Nandhini Swaminathan; David Danks (CoRR, 2024)

Bias Mitigation via Compensation: A Reinforcement Learning Perspective. - Nandhini Swaminathan; David Danks (CoRR, 2024)

Addressing the Unforeseen Harms of Technology CCC Whitepaper. - Nadya Bliss; Kevin Butler; David Danks; Ufuk Topcu; Matthew Turk 002 (CoRR, 2024)

Navigating the sociotechnical labyrinth: Dynamic certification for responsible embodied AI. - Georgios Bakirtzis; Andrea Aler Tubella; Andreas Theodorou; David Danks; Ufuk Topcu (CoRR, 2024)

ION-C: Integration of Overlapping Networks via Constraints. - Praveen Nair; Payal Bhandari; Mohammadsajad Abavisani; Sergey M. Plis; David Danks (CoRR, 2024)

Governing Ethical Gaps in Distributed AI Development. - Nandhini Swaminathan; David Danks (Digit. Soc., 2024)

Tackling AI Hyping. - Mona Sloane; David Danks; Emanuel Moss (AI Ethics, 2024)

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 008; Stone Tao; Mengyang Liu; et al. (ACL (Findings), 2025)

Discrimination Exposed? On the Reliability of Explanations for Discrimination Detection. - Julian Skirzynski; David Danks; Berk Ustun (FAccT, 2025)

Trustworthiness in Stochastic Systems: Towards Opening the Black Box. - Jennifer Chien; David Danks (CoRR, 2025)

Enabling the AI Revolution in Healthcare. - Mona Singh 001; Katie A. Siek; David Danks; Rayid Ghani; Haley Grin; Brian LaMacchia; Daniel Lopresti; Tammy Toscos (CoRR, 2025)

Causal Graph Recovery in Neuroimaging through Answer Set Programming. - Mohammadsajad Abavisani; Kseniya Solovyeva; David Danks; Vince D. Calhoun; Sergey M. Plis (CoRR, 2025)

Now More Than Ever, Foundational AI Research and Infrastructure Depends on the Federal Government. - Michela Taufer; Rada Mihalcea; Matthew Turk 002; Daniel P. Lopresti; Adam Wierman; Kevin Butler; Sven Koenig; David Danks; William Gropp; Manish Parashar; Yolanda Gil; William C. Regli; et al. (CoRR, 2025)