David S. Watson

Orcid: 0000-0001-9632-2159

According to our database1, David S. Watson authored at least 27 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
A Genealogical Approach to Algorithmic Bias.
Minds Mach., June, 2024

Bounding Causal Effects with Leaky Instruments.
CoRR, 2024

2023
The Ethics of Online Controlled Experiments (A/B Testing).
Minds Mach., December, 2023

Correction to: The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems.
Minds Mach., March, 2023

The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems.
Minds Mach., March, 2023

Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values.
Proceedings of the Explainable Artificial Intelligence, 2023

Explaining Predictive Uncertainty with Information Theoretic Shapley Values.
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.
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.
Proceedings of the Conference on Causal Learning and Reasoning, 2023

Adversarial Random Forests for Density Estimation and Generative Modeling.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice.
Minds Mach., 2022

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

Conditional Feature Importance for Mixed Data.
CoRR, 2022

Smooth densities and generative modeling with unsupervised random forests.
CoRR, 2022

Causal discovery under a confounder blanket.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Rational Shapley Values.
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.
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.
PhD thesis, 2021

The explanation game: a formal framework for interpretable machine learning.
Synth., 2021

Testing conditional independence in supervised learning algorithms.
Mach. Learn., 2021

Operationalizing Complex Causes: A Pragmatic View of Mediation.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Spectrum: fast density-aware spectral clustering for single and multi-omic data.
Bioinform., 2020

Causal Feature Learning for Utility-Maximizing Agents.
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.
Big Data Soc., January, 2019

The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence.
Minds Mach., 2019

Testing Conditional Predictive Independence in Supervised Learning Algorithms.
CoRR, 2019

2018
Crowdsourced science: sociotechnical epistemology in the e-research paradigm.
Synth., 2018


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