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2020 – today
- 2024
[j19]Mona Sloane
, David Danks, Emanuel Moss:
Tackling AI Hyping. AI Ethics 4(3): 669-677 (2024)- [j18]Nandhini Swaminathan, David Danks:
Governing Ethical Gaps in Distributed AI Development. Digit. Soc. 3(1): 7 (2024) - [c35]Jennifer Chien, David Danks:
Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation. FAccT 2024: 933-946 - [i14]Jennifer Chien, David Danks:
Beyond Behaviorist Representational Harms: A Plan for Measurement and Mitigation. CoRR abs/2402.01705 (2024) - [i13]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 abs/2402.01762 (2024) - [i12]David Danks, Rada Mihalcea, Katie Siek, Mona Singh, Brian Dixon, Haley Griffin:
Future of Pandemic Prevention and Response CCC Workshop Report. CoRR abs/2403.00096 (2024) - [i11]Nandhini Swaminathan, David Danks:
Application of the NIST AI Risk Management Framework to Surveillance Technology. CoRR abs/2403.15646 (2024) - [i10]Nandhini Swaminathan, David Danks:
Bias Mitigation via Compensation: A Reinforcement Learning Perspective. CoRR abs/2404.19256 (2024) - [i9]Nadya Bliss, Kevin Butler, David Danks, Ufuk Topcu, Matthew Turk:
Addressing the Unforeseen Harms of Technology CCC Whitepaper. CoRR abs/2408.06431 (2024) - [i8]Georgios Bakirtzis, Andrea Aler Tubella, Andreas Theodorou, David Danks, Ufuk Topcu:
Navigating the sociotechnical labyrinth: Dynamic certification for responsible embodied AI. CoRR abs/2409.00015 (2024) - [i7]Nandhini Swaminathan, David Danks:
Identification and Mitigating Bias in Quantum Machine Learning. CoRR abs/2409.19011 (2024) - 2023
- [j17]Thilo Hagendorff, David Danks:
Ethical and methodological challenges in building morally informed AI systems. AI Ethics 3(2): 553-566 (2023) - [j16]Georgios Bakirtzis, Steven Carr, David Danks, Ufuk Topcu:
Dynamic Certification for Autonomous Systems. Commun. ACM 66(9): 64-72 (2023) - [j15]Daniel Trusilo, David Danks:
Artificial intelligence and humanitarian obligations. Ethics Inf. Technol. 25(1): 12 (2023) - [c34]Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey M. Plis:
Causal Learning through Deliberate Undersampling. CLeaR 2023: 518-530 - [c33]Phuong (Phoebe) Dinh, David Danks:
Expectations of Determinism Underlie Domain Effects on Adult Causal Learning. CogSci 2023 - [c32]Mohammadsajad Abavisani, David Danks, Sergey M. Plis:
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints. ICLR 2023 - [c31]Anisha Bontula, David Danks, Naomi T. Fitter:
The Ambiguity of Robot Rights. ICSR (1) 2023: 204-215 - [i6]Jennifer Chien, David Danks:
Fairness Vs. Personalization: Towards Equity in Epistemic Utility. CoRR abs/2309.11503 (2023) - 2022
- [j14]David Danks, Daniel Trusilo:
The Challenge of Ethical Interoperability. Digit. Soc. 1(2) (2022) - [c30]Phuong (Phoebe) Dinh, David Danks:
Expectations of Causal Determinism in Causal Learning. CogSci 2022 - [c29]Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta, Zachary C. Lipton, David Danks:
Homophily and Incentive Effects in Use of Algorithms. CogSci 2022 - [i5]Georgios Bakirtzis, Steven Carr, David Danks, Ufuk Topcu:
Dynamic Certification for Autonomous Systems. CoRR abs/2203.10950 (2022) - [i4]Mohammadsajad Abavisani, David Danks, Sergey M. Plis:
Constraint-Based Causal Structure Learning from Undersampled Graphs. CoRR abs/2205.09235 (2022) - [i3]Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta, Zachary C. Lipton, David Danks:
Homophily and Incentive Effects in Use of Algorithms. CoRR abs/2205.09701 (2022) - 2021
- [j13]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): 101-125 (2021) - [j12]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): 650-652 (2021) - [j11]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): 566-571 (2021) - [c28]Paige Golden, David Danks:
Ethical Obligations to Provide Novelty. AIES 2021: 502-508 - [c27]Corey J. Cusimano, Natalia C. Zorrilla, David Danks, Tania Lombrozo:
Reason-Based Constraint in Theory of Mind. CogSci 2021 - [c26]Laila Johnston, Noah Hillman, David Danks:
Individual Differences in Causal Learning. CogSci 2021 - 2020
- [c25]Joy Lu, Dokyun Lee, Tae Wan Kim, David Danks:
Good Explanation for Algorithmic Transparency. AIES 2020: 93 - [c24]Yishan Zhou, David Danks:
Different "Intelligibility" for Different Folks. AIES 2020: 194-199 - [c23]Phuong (Phoebe) Dinh, David Danks:
Effects of Causal Determinism on Causal Learning Trajectories. CogSci 2020
2010 – 2019
- 2019
- [j10]David Danks, Sergey M. Plis:
Amalgamating evidence of dynamics. Synth. 196(8): 3213-3230 (2019) - [c22]Timothy Geary, David Danks:
Balancing the Benefits of Autonomous Vehicles. AIES 2019: 181-186 - [c21]Jack Parker, David Danks:
How Technological Advances Can Reveal Rights. AIES 2019: 201 - [c20]David Danks:
The Value of Trustworthy AI. AIES 2019: 521-522 - 2018
- [c19]Emily LaRosa, David Danks:
Impacts on Trust of Healthcare AI. AIES 2018: 210-215 - [c18]Alex John London, David Danks:
Regulating Autonomous Vehicles: A Policy Proposal. AIES 2018: 216-221 - [c17]Michael Henry Tessler, Noah D. Goodman, David Danks, Emily Foster-Hanson, Marjorie Rhodes, Greg Carlson:
Generalizations, from representation to transmission. CogSci 2018 - 2017
- [j9]David Danks, Alex John London:
Regulating Autonomous Systems: Beyond Standards. IEEE Intell. Syst. 32(1): 88-91 (2017) - [j8]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: 208-225 (2017) - [c16]Rick Kazman, Robert Stoddard, David Danks, Yuanfang Cai:
Causal modeling, discovery & inference for software engineering. ICSE (Companion Volume) 2017: 172-174 - [c15]David Danks, Alex John London:
Algorithmic Bias in Autonomous Systems. IJCAI 2017: 4691-4697 - 2016
- [j7]Sarah Wellen, David Danks:
Adaptively Rational Learning. Minds Mach. 26(1-2): 87-102 (2016) - [c14]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: 216-227 - [i2]Antti Hyttinen, Sergey M. Plis, Matti Järvisalo, Frederick Eberhardt, David Danks:
Causal Discovery from Subsampled Time Series Data by Constraint Optimization. CoRR abs/1602.07970 (2016) - 2015
- [j6]David Danks:
Goal-dependence in (scientific) ontology. Synth. 192(11): 3601-3616 (2015) - [c13]Sergey M. Plis, David Danks, Cynthia Freeman, Vince D. Calhoun:
Rate-Agnostic (Causal) Structure Learning. NIPS 2015: 3303-3311 - [c12]Sergey M. Plis, David Danks, Jianyu Yang:
Mesochronal Structure Learning. UAI 2015: 702-711 - 2014
- [j5]Erich Kummerfeld, David Danks:
Model change and reliability in scientific inference. Synth. 191(12): 2673-2693 (2014) - [j4]Erich Kummerfeld, David Danks:
Erratum to: Model change and methodological virtues in scientific inference. Synth. 191(14): 3469-3472 (2014) - [c11]Sarah Wellen, David Danks:
Learning with a Purpose: The Influence of Goals. CogSci 2014 - 2013
- [j3]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): 695-723 (2013) - [c10]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 - [c9]David Danks:
Moving from Levels & Reduction to Dimensions & Constraints. CogSci 2013 - [c8]Erich Kummerfeld, David Danks:
Tracking Time-varying Graphical Structure. NIPS 2013: 1205-1213 - [i1]David Danks, Clark Glymour:
Linearity Properties of Bayes Nets with Binary Variables. CoRR abs/1301.2263 (2013) - 2012
- [c7]Sarah Wellen, David Danks:
Actor-Observer Asymmetries in Judgments of Intentional Actions. CogSci 2012 - [c6]Sarah Wellen, David Danks:
Learning Causal Structure through Local Prediction-error Learning. CogSci 2012 - 2011
- [j2]Frederick Eberhardt, David Danks:
Confirmation in the Cognitive Sciences: The Problematic Case of Bayesian Models. Minds Mach. 21(3): 389-410 (2011) - 2010
- [j1]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): 169-192 (2010)
2000 – 2009
- 2008
- [c5]Robert E. Tillman, David Danks, Clark Glymour:
Integrating Locally Learned Causal Structures with Overlapping Variables. NIPS 2008: 1665-1672 - 2004
- [c4]David Danks:
Constraint-Based Human Causal Learning. ICCM 2004: 342-343 - 2002
- [c3]David Danks:
Learning the Causal Structure of Overlapping Variable Sets. Discovery Science 2002: 178-191 - [c2]David Danks, Thomas L. Griffiths, Joshua B. Tenenbaum:
Dynamical Causal Learning. NIPS 2002: 67-74 - 2001
- [c1]David Danks, Clark Glymour:
Linearity Properties of Bayes Nets with Binary Variables. UAI 2001: 98-104
Coauthor Index
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last updated on 2024-11-05 22:04 CET by the dblp team
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