David S. Watson

According to our database, David S. Watson authored at least 27 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Genealogical Approach to Algorithmic Bias.

Marta Ziosi, David S. Watson, Luciano Floridi

Minds Mach., June, 2024

Bounding Causal Effects with Leaky Instruments.

David S. Watson, Jordan Penn, Lee M. Gunderson, Gecia Bravo Hermsdorff, Afsaneh Mastouri, Ricardo Silva

CoRR, 2024

2023

The Ethics of Online Controlled Experiments (A/B Testing).

Andrea Polonioli, Riccardo Ghioni, Ciro Greco, Prathm Juneja, Jacopo Tagliabue, David S. Watson, Luciano Floridi

Minds Mach., December, 2023

Correction to: The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems.

Jakob Mökander, Margi Sheth, David S. Watson, Luciano Floridi

Minds Mach., March, 2023

The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems.

Jakob Mökander, Margi Sheth, David S. Watson, Luciano Floridi

Minds Mach., March, 2023

Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values.

Kristin Blesch, Marvin N. Wright, David S. Watson

Proceedings of the Explainable Artificial Intelligence, 2023

Explaining Predictive Uncertainty with Information Theoretic Shapley Values.

David S. Watson, Joshua O'Hara, Niek Tax, Richard Mudd, Ido Guy

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Intervention Generalization: A View from Factor Graph Models.

Gecia Bravo Hermsdorff, David S. Watson, Jialin Yu, Jakob Zeitler, Ricardo Silva

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Stochastic Causal Programming for Bounding Treatment Effects.

Kirtan Padh, Jakob Zeitler, David S. Watson, Matt J. Kusner, Ricardo Silva, Niki Kilbertus

Proceedings of the Conference on Causal Learning and Reasoning, 2023

Adversarial Random Forests for Density Estimation and Generative Modeling.

David S. Watson, Kristin Blesch, Jan Kapar, Marvin N. Wright

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice.

David S. Watson, Limor Gultchin, Ankur Taly, Luciano Floridi

Minds Mach., 2022

The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other?

Jakob Mökander, Prathm Juneja, David S. Watson, Luciano Floridi

Minds Mach., 2022

Conditional Feature Importance for Mixed Data.

Kristin Blesch, David S. Watson, Marvin N. Wright

CoRR, 2022

Smooth densities and generative modeling with unsupervised random forests.

David S. Watson, Kristin Blesch, Jan Kapar, Marvin N. Wright

CoRR, 2022

Causal discovery under a confounder blanket.

David S. Watson, Ricardo Silva

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Rational Shapley Values.

David S. Watson

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Models for Classifying AI Systems: the Switch, the Ladder, and the Matrix.

Jakob Mökander, Margi Sheth, David S. Watson, Luciano Floridi

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

2021

Explaining black box algorithms: epistemological challenges and machine learning solutions.

David S. Watson

PhD thesis, 2021

The explanation game: a formal framework for interpretable machine learning.

David S. Watson, Luciano Floridi

Synth., 2021

Testing conditional independence in supervised learning algorithms.

David S. Watson, Marvin N. Wright

Mach. Learn., 2021

Operationalizing Complex Causes: A Pragmatic View of Mediation.

Limor Gultchin, David S. Watson, Matt J. Kusner, Ricardo Silva

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Spectrum: fast density-aware spectral clustering for single and multi-omic data.

Christopher R. John, David S. Watson, Michael R. Barnes, Costantino Pitzalis, Myles J. Lewis

Bioinform., 2020

Causal Feature Learning for Utility-Maximizing Agents.

David Kinney, David S. Watson

Proceedings of the International Conference on Probabilistic Graphical Models, 2020

2019

Are the dead taking over Facebook? A Big Data approach to the future of death online.

Carl Öhman, David S. Watson

Big Data Soc., January, 2019

The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence.

David S. Watson

Minds Mach., 2019

Testing Conditional Predictive Independence in Supervised Learning Algorithms.

David S. Watson, Marvin N. Wright

CoRR, 2019

2018

Crowdsourced science: sociotechnical epistemology in the e-research paradigm.

David S. Watson, Luciano Floridi

Synth., 2018