David Danks

82 papers A* 2A 2B 14C 8Misc 1Journal 42Unranked 13
YearRankTypeTitle / Venue / Authors
2026 J jnl
Combining fast and slow fMRI sampling rates can enhance predictive power in resting-state data.
NeuroImage
Joanne Wardell, Kseniya Solovyeva, David Danks, Niko Huotari, Vesa Kiviniemi, Vesa Korhonen, Thomas DeRamus, Godfrey D. Pearlson, Vince D. Calhoun, Sergey M. Plis
2026 J jnl
HiVAE: Hierarchical Latent Variables for Scalable Theory of Mind.
CoRR
Nigel Doering, Rahath Malladi, Arshia Sangwan, David Danks, Tauhidur Rahman
2026 J jnl
Multi-Use AI and Moral Responsibility.
Commun. ACM
Daniel Trusilo, David Danks
2025 J jnl
Causal Graph Recovery in Neuroimaging through Answer Set Programming.
CoRR
Mohammadsajad Abavisani, Kseniya Solovyeva, David Danks, Vince D. Calhoun, Sergey M. Plis
2025 — conf
DECIFRA: Deep Extraction of Causally Informed Features via Restricted Architecture.
MLSP
Pavel Popov, Cristian Morasso, Giorgio Dolci, Ilaria Boscolo Galazzo, Godfrey D. Pearlson, David Danks, Vince D. Calhoun, Gloria Menegaz, Sergey M. Plis
2025 — conf
Discrimination Exposed? On the Reliability of Explanations for Discrimination Detection.
FAccT
Julian Skirzynski, David Danks, Berk Ustun
2025 — conf
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation.
ACL (Findings)
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
2025 J jnl
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation.
CoRR
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
2025 J jnl
Enabling the AI Revolution in Healthcare.
CoRR
Mona Singh, Katie A. Siek, David Danks, Rayid Ghani, Haley Grin, Brian LaMacchia, Daniel Lopresti, Tammy Toscos
2025 J jnl
Now More Than Ever, Foundational AI Research and Infrastructure Depends on the Federal Government.
CoRR
Michela Taufer, Rada Mihalcea, Matthew Turk, Daniel P. Lopresti, Adam Wierman, Kevin R. B. Butler, Sven Koenig, David Danks, William Gropp, Manish Parashar, Yolanda Gil, William C. Regli, Rajmohan Rajaraman, David Jensen, Nadya Bliss, Mary Lou Maher
2025 J jnl
Trustworthiness in Stochastic Systems: Towards Opening the Black Box.
CoRR
Jennifer Chien, David Danks
2024 J jnl
Addressing the Unforeseen Harms of Technology CCC Whitepaper.
CoRR
Nadya Bliss, Kevin R. B. Butler, David Danks, Ufuk Topcu, Matthew Turk
2024 J jnl
Application of the NIST AI Risk Management Framework to Surveillance Technology.
CoRR
Nandhini Swaminathan, David Danks
2024 — conf
Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation.
FAccT
Jennifer Chien, David Danks
2024 J jnl
Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation.
CoRR
Jennifer Chien, David Danks
2024 J jnl
Bias Mitigation via Compensation: A Reinforcement Learning Perspective.
CoRR
Nandhini Swaminathan, David Danks
2024 J jnl
Commercial AI, Conflict, and Moral Responsibility: A theoretical analysis and practical approach to the moral responsibilities associated with dual-use AI technology.
CoRR
Daniel Trusilo, David Danks
2024 B conf
Dynamics of Causal Attribution.
CogSci
Dana Kulzhabayeva, Joseph Jay Williams, David Danks
2024 J jnl
Future of Pandemic Prevention and Response CCC Workshop Report.
CoRR
David Danks, Rada Mihalcea, Katie A. Siek, Mona Singh, Brian Dixon, Haley Griffin
2024 J jnl
Governing Ethical Gaps in Distributed AI Development.
Digit. Soc.
Nandhini Swaminathan, David Danks
2024 J jnl
ION-C: Integration of Overlapping Networks via Constraints.
CoRR
Praveen Nair, Payal Bhandari, Mohammadsajad Abavisani, Sergey M. Plis, David Danks
2024 Misc conf
Identification and Mitigating Bias in Quantum Machine Learning.
QCE
Nandhini Swaminathan, David Danks
2024 J jnl
Identification and Mitigating Bias in Quantum Machine Learning.
CoRR
Nandhini Swaminathan, David Danks
2024 J jnl
Navigating the sociotechnical labyrinth: Dynamic certification for responsible embodied AI.
CoRR
Georgios Bakirtzis, Andrea Aler Tubella, Andreas Theodorou, David Danks, Ufuk Topcu
2024 J jnl
Tackling AI Hyping.
AI Ethics
Mona Sloane, David Danks, Emanuel Moss
2023 J jnl
Artificial intelligence and humanitarian obligations.
Ethics Inf. Technol.
Daniel Trusilo, David Danks
2023 — conf
Causal Learning through Deliberate Undersampling.
CLeaR
Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey M. Plis
2023 J jnl
Dynamic Certification for Autonomous Systems.
Commun. ACM
Georgios Bakirtzis, Steven Carr, David Danks, Ufuk Topcu
2023 J jnl
Ethical and methodological challenges in building morally informed AI systems.
AI Ethics
Thilo Hagendorff, David Danks
2023 B conf
Expectations of Determinism Underlie Domain Effects on Adult Causal Learning.
CogSci
Phuong (Phoebe) Dinh, David Danks
2023 J jnl
Fairness Vs. Personalization: Towards Equity in Epistemic Utility.
CoRR
Jennifer Chien, David Danks
2023 A* conf
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints.
ICLR
Mohammadsajad Abavisani, David Danks, Sergey M. Plis
2023 — conf
The Ambiguity of Robot Rights.
ICSR (1)
Anisha Bontula, David Danks, Naomi T. Fitter
2022 J jnl
Constraint-Based Causal Structure Learning from Undersampled Graphs.
CoRR
Mohammadsajad Abavisani, David Danks, Sergey M. Plis
2022 J jnl
Dynamic Certification for Autonomous Systems.
CoRR
Georgios Bakirtzis, Steven Carr, David Danks, Ufuk Topcu
2022 B conf
Expectations of Causal Determinism in Causal Learning.
CogSci
Phuong (Phoebe) Dinh, David Danks
2022 B conf
Homophily and Incentive Effects in Use of Algorithms.
CogSci
Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta, Zachary C. Lipton, David Danks
2022 J jnl
Homophily and Incentive Effects in Use of Algorithms.
CoRR
Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta, Zachary C. Lipton, David Danks
2022 J jnl
The Challenge of Ethical Interoperability.
Digit. Soc.
David Danks, Daniel Trusilo
2021 J jnl
AI4People: Ethical Guidelines for the Automotive Sector - Fundamental Requirements and Practical Recommendations.
Int. J. Technoethics
Christoph Lütge, Franziska Poszler, Aida Joaquin Acosta, David Danks, Gail Gottehrer, Lucian Mihet-Popa, Aisha Naseer
2021 C conf
Ethical Obligations to Provide Novelty.
AIES
Paige Golden, David Danks
2021 J jnl
Governing AI safety through independent audits.
Nat. Mach. Intell.
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
2021 B conf
Individual Differences in Causal Learning.
CogSci
Laila Johnston, Noah Hillman, David Danks
2021 B conf
Reason-Based Constraint in Theory of Mind.
CogSci
Corey J. Cusimano, Natalia C. Zorrilla, David Danks, Tania Lombrozo
2021 J jnl
The case for information fiduciaries: The implementation of a data ethics checklist at Seattle Children's Hospital.
J. Am. Medical Informatics Assoc.
Elizabeth Montague, T. Eugene Day, Dwight Barry, Maria Brumm, Aaron McAdie, Andrew B. Cooper, Julia Wignall, Steve Erdman, Diahnna Núñez, Douglas Diekema, David Danks
2020 C conf
Different "Intelligibility" for Different Folks.
AIES
Yishan Zhou, David Danks
2020 B conf
Effects of Causal Determinism on Causal Learning Trajectories.
CogSci
Phuong (Phoebe) Dinh, David Danks
2020 C conf
Good Explanation for Algorithmic Transparency.
AIES
Joy Lu, Dokyun Lee, Tae Wan Kim, David Danks
2019 J jnl
Amalgamating evidence of dynamics.
Synth.
David Danks, Sergey M. Plis
2019 C conf
Balancing the Benefits of Autonomous Vehicles.
AIES
Timothy Geary, David Danks
2019 C conf
How Technological Advances Can Reveal Rights.
AIES
Jack Parker, David Danks
2019 C conf
The Value of Trustworthy AI.
AIES
David Danks
2018 B conf
Generalizations, from representation to transmission.
CogSci
Michael Henry Tessler, Noah D. Goodman, David Danks, Emily Foster-Hanson, Marjorie Rhodes, Greg Carlson
2018 C conf
Impacts on Trust of Healthcare AI.
AIES
Emily LaRosa, David Danks
2018 C conf
Regulating Autonomous Vehicles: A Policy Proposal.
AIES
Alex John London, David Danks
2017 J jnl
A constraint optimization approach to causal discovery from subsampled time series data.
Int. J. Approx. Reason.
Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
2017 A* conf
Algorithmic Bias in Autonomous Systems.
IJCAI
David Danks, Alex John London
2017 — conf
Causal modeling, discovery & inference for software engineering.
ICSE (Companion Volume)
Rick Kazman, Robert Stoddard, David Danks, Yuanfang Cai
2017 J jnl
Regulating Autonomous Systems: Beyond Standards.
IEEE Intell. Syst.
David Danks, Alex John London
2016 J jnl
Adaptively Rational Learning.
Minds Mach.
Sarah Wellen, David Danks
2016 — conf
Causal Discovery from Subsampled Time Series Data by Constraint Optimization.
Probabilistic Graphical Models
Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
2016 J jnl
Causal Discovery from Subsampled Time Series Data by Constraint Optimization.
CoRR
Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
2015 J jnl
Goal-dependence in (scientific) ontology.
Synth.
David Danks
2015 A conf
Mesochronal Structure Learning.
UAI
Sergey M. Plis, David Danks, Jianyu Yang
2015 — conf
Rate-Agnostic (Causal) Structure Learning.
NIPS
Sergey M. Plis, David Danks, Cynthia Freeman, Vince D. Calhoun
2014 J jnl
Erratum to: Model change and methodological virtues in scientific inference.
Synth.
Erich Kummerfeld, David Danks
2014 B conf
Learning with a Purpose: The Influence of Goals.
CogSci
Sarah Wellen, David Danks
2014 J jnl
Model change and reliability in scientific inference.
Synth.
Erich Kummerfeld, David Danks
2013 J jnl
Linearity Properties of Bayes Nets with Binary Variables
CoRR
David Danks, Clark Glymour
2013 B conf
Moving from Levels & Reduction to Dimensions & Constraints.
CogSci
David Danks
2013 — conf
Tracking Time-varying Graphical Structure.
NIPS
Erich Kummerfeld, David Danks
2013 B conf
What if? Counterfactual reasoning, pretense, and the role of possible worlds.
CogSci
Daphna Buchsbaum, Caren M. Walker, Alison Gopnik, Nick Chater, David Danks, Christopher G. Lucas, Charles Kemp, Eva Rafetseder, Josef Perner
2013 J jnl
Wisdom of crowds versus groupthink: learning in groups and in isolation.
Int. J. Game Theory
Conor Mayo-Wilson, Kevin J. S. Zollman, David Danks
2012 B conf
Actor-Observer Asymmetries in Judgments of Intentional Actions.
CogSci
Sarah Wellen, David Danks
2012 B conf
Learning Causal Structure through Local Prediction-error Learning.
CogSci
Sarah Wellen, David Danks
2011 J jnl
Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models.
Minds Mach.
Frederick Eberhardt, David Danks
2010 J jnl
Actual causation: a stone soup essay.
Synth.
Clark Glymour, David Danks, Bruce Glymour, Frederick Eberhardt, Joseph D. Ramsey, Richard Scheines, Peter Spirtes, Choh Man Teng, Jiji Zhang
2008 — conf
Integrating Locally Learned Causal Structures with Overlapping Variables.
NIPS
Robert E. Tillman, David Danks, Clark Glymour
2004 — conf
Constraint-Based Human Causal Learning.
ICCM
David Danks
2002 — conf
Dynamical Causal Learning.
NIPS
David Danks, Thomas L. Griffiths, Joshua B. Tenenbaum
2002 B conf
Learning the Causal Structure of Overlapping Variable Sets.
Discovery Science
David Danks
2001 A conf
Linearity Properties of Bayes Nets with Binary Variables.
UAI
David Danks, Clark Glymour