David Danks

Publication Activity (10 Years)

Years Active: 2001-2024
Publications (10 Years): 44

Top Topics

Conditional Independence Tests

Structure Learning

Causal Discovery

Top Venues

Publications

Nandhini Swaminathan, David Danks
Bias Mitigation via Compensation: A Reinforcement Learning Perspective. CoRR (2024)
Nandhini Swaminathan, David Danks
Application of the NIST AI Risk Management Framework to Surveillance Technology. CoRR (2024)
Jennifer Chien, David Danks
Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation. CoRR (2024)
Jennifer Chien, David Danks
Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation. FAccT (2024)
David Danks, Rada Mihalcea, Katie Siek, Mona Singh, Brian Dixon, Haley Griffin
Future of Pandemic Prevention and Response CCC Workshop Report. CoRR (2024)
Daniel Trusilo, David Danks
Commercial AI, Conflict, and Moral Responsibility: A theoretical analysis and practical approach to the moral responsibilities associated with dual-use AI technology. CoRR (2024)
Nandhini Swaminathan, David Danks
Governing Ethical Gaps in Distributed AI Development. Digit. Soc. 3 (1) (2024)
Daniel Trusilo, David Danks
Artificial intelligence and humanitarian obligations. Ethics Inf. Technol. 25 (1) (2023)
Georgios Bakirtzis, Steven Carr, David Danks, Ufuk Topcu
Dynamic Certification for Autonomous Systems. Commun. ACM 66 (9) (2023)
Jennifer Chien, David Danks
Fairness Vs. Personalization: Towards Equity in Epistemic Utility. CoRR (2023)
Anisha Bontula, David Danks, Naomi T. Fitter
The Ambiguity of Robot Rights. ICSR (1) (2023)
Mohammadsajad Abavisani, David Danks, Sergey M. Plis
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints. ICLR (2023)
Phuong (Phoebe) Dinh, David Danks
Expectations of Determinism Underlie Domain Effects on Adult Causal Learning. CogSci (2023)
Thilo Hagendorff, David Danks
Ethical and methodological challenges in building morally informed AI systems. AI Ethics 3 (2) (2023)
Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey M. Plis
Causal Learning through Deliberate Undersampling. CLeaR (2023)
Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta, Zachary C. Lipton, David Danks
Homophily and Incentive Effects in Use of Algorithms. CoRR (2022)
Mohammadsajad Abavisani, David Danks, Sergey M. Plis
Constraint-Based Causal Structure Learning from Undersampled Graphs. CoRR (2022)
David Danks, Daniel Trusilo
The Challenge of Ethical Interoperability. Digit. Soc. 1 (2) (2022)
Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta, Zachary C. Lipton, David Danks
Homophily and Incentive Effects in Use of Algorithms. CogSci (2022)
Phuong (Phoebe) Dinh, David Danks
Expectations of Causal Determinism in Causal Learning. CogSci (2022)
Georgios Bakirtzis, Steven Carr, David Danks, Ufuk Topcu
Dynamic Certification for Autonomous Systems. CoRR (2022)
Paige Golden, David Danks
Ethical Obligations to Provide Novelty. AIES (2021)
Gregory Falco, Ben Shneiderman, Julia Badger, Ryan Carrier, Anton Dahbura, David Danks, Martin Eling, Alwyn Goodloe, Jerry Gupta, Christopher Hart, Marina Jirotka, Henric Johnson, Cara Lapointe, Ashley J. Llorens, Alan K. Mackworth, Carsten Maple, Sigurður Emil Pálsson, Frank Pasquale, Alan F. T. Winfield, Zee Kin Yeong
Governing AI safety through independent audits. Nat. Mach. Intell. 3 (7) (2021)
Elizabeth Montague, T. Eugene Day, Dwight Barry, Maria Brumm, Aaron McAdie, Andrew B. Cooper, Julia Wignall, Steve Erdman, Diahnna Núñez, Douglas Diekema, David Danks
The case for information fiduciaries: The implementation of a data ethics checklist at Seattle Children's Hospital. J. Am. Medical Informatics Assoc. 28 (3) (2021)
Christoph Lütge, Franziska Poszler, Aida Joaquin Acosta, David Danks, Gail Gottehrer, Lucian Mihet-Popa, Aisha Naseer
AI4People: Ethical Guidelines for the Automotive Sector - Fundamental Requirements and Practical Recommendations. Int. J. Technoethics 12 (1) (2021)
Corey J. Cusimano, Natalia C. Zorrilla, David Danks, Tania Lombrozo
Reason-Based Constraint in Theory of Mind. CogSci (2021)
Laila Johnston, Noah Hillman, David Danks
Individual Differences in Causal Learning. CogSci (2021)
Joy Lu, Dokyun (DK) Lee, Tae Wan Kim, David Danks
Good Explanation for Algorithmic Transparency. AIES (2020)
Yishan Zhou, David Danks
Different "Intelligibility" for Different Folks. AIES (2020)
Phuong (Phoebe) Dinh, David Danks
Effects of Causal Determinism on Causal Learning Trajectories. CogSci (2020)
Jack Parker, David Danks
How Technological Advances Can Reveal Rights. AIES (2019)
David Danks, Sergey M. Plis
Amalgamating evidence of dynamics. Synth. 196 (8) (2019)
Timothy Geary, David Danks
Balancing the Benefits of Autonomous Vehicles. AIES (2019)
David Danks
The Value of Trustworthy AI. AIES (2019)
Emily LaRosa, David Danks
Impacts on Trust of Healthcare AI. AIES (2018)
Michael Henry Tessler, Noah D. Goodman, David Danks, Emily Foster Hanson, Marjorie Rhodes, Greg Carlson
Generalizations, from representation to transmission. CogSci (2018)
Alex John London, David Danks
Regulating Autonomous Vehicles: A Policy Proposal. AIES (2018)
Rick Kazman, Robert Stoddard, David Danks, Yuanfang Cai
Causal modeling, discovery & inference for software engineering. ICSE (Companion Volume) (2017)
David Danks, Alex John London
Regulating Autonomous Systems: Beyond Standards. IEEE Intell. Syst. 32 (1) (2017)
Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
A constraint optimization approach to causal discovery from subsampled time series data. Int. J. Approx. Reason. 90 (2017)
David Danks, Alex John London
Algorithmic Bias in Autonomous Systems. IJCAI (2017)
Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
Causal Discovery from Subsampled Time Series Data by Constraint Optimization. Probabilistic Graphical Models (2016)
Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
Causal Discovery from Subsampled Time Series Data by Constraint Optimization. CoRR (2016)
Sarah Wellen, David Danks
Adaptively Rational Learning. Minds Mach. 26 (1-2) (2016)
Sergey M. Plis, David Danks, Jianyu Yang
Mesochronal Structure Learning. UAI (2015)
David Danks
Goal-dependence in (scientific) ontology. Synth. 192 (11) (2015)
Sergey M. Plis, David Danks, Cynthia Freeman, Vince D. Calhoun
Rate-Agnostic (Causal) Structure Learning. NIPS (2015)
Erich Kummerfeld, David Danks
Erratum to: Model change and methodological virtues in scientific inference. Synth. 191 (14) (2014)
Sarah Wellen, David Danks
Learning with a Purpose: The Influence of Goals. CogSci (2014)
Erich Kummerfeld, David Danks
Model change and reliability in scientific inference. Synth. 191 (12) (2014)
Conor Mayo-Wilson, Kevin J. S. Zollman, David Danks
Wisdom of crowds versus groupthink: learning in groups and in isolation. Int. J. Game Theory 42 (3) (2013)
Daphna Buchsbaum, Caren M. Walker, Alison Gopnik, Nick Chater, David Danks, Christopher G. Lucas, Charles Kemp, Eva Rafetseder, Josef Perner
What if? Counterfactual reasoning, pretense, and the role of possible worlds. CogSci (2013)
David Danks, Clark Glymour
Linearity Properties of Bayes Nets with Binary Variables CoRR (2013)
Erich Kummerfeld, David Danks
Tracking Time-varying Graphical Structure. NIPS (2013)
David Danks
Moving from Levels & Reduction to Dimensions & Constraints. CogSci (2013)
Sarah Wellen, David Danks
Learning Causal Structure through Local Prediction-error Learning. CogSci (2012)
Sarah Wellen, David Danks
Actor-Observer Asymmetries in Judgments of Intentional Actions. CogSci (2012)
Frederick Eberhardt, David Danks
Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. Minds Mach. 21 (3) (2011)
Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph D. Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng, Jiji Zhang
Actual causation: a stone soup essay. Synth. 175 (2) (2010)
Robert E. Tillman, David Danks, Clark Glymour
Integrating Locally Learned Causal Structures with Overlapping Variables. NIPS (2008)
David Danks
Constraint-Based Human Causal Learning. ICCM (2004)
David Danks, Thomas L. Griffiths, Joshua B. Tenenbaum
Dynamical Causal Learning. NIPS (2002)
David Danks
Learning the Causal Structure of Overlapping Variable Sets. Discovery Science (2002)
David Danks, Clark Glymour
Linearity Properties of Bayes Nets with Binary Variables. UAI (2001)