Crowdsourced science: sociotechnical epistemology in the e-research paradigm. - David S. Watson; Luciano Floridi (Synth., 2018)

Testing Conditional Predictive Independence in Supervised Learning Algorithms. - David S. Watson; Marvin N. Wright (CoRR, 2019)

The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence. - David S. Watson (Minds Mach., 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., 2019)

Causal Feature Learning for Utility-Maximizing Agents. - David Kinney; David S. Watson (PGM, 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)

Operationalizing Complex Causes: A Pragmatic View of Mediation. - Limor Gultchin; David S. Watson; Matt J. Kusner; Ricardo Silva 001 (ICML, 2021)

Testing conditional independence in supervised learning algorithms. - David S. Watson; Marvin N. Wright (Mach. Learn., 2021)

The explanation game: a formal framework for interpretable machine learning. - David S. Watson; Luciano Floridi (Synth., 2021)

Explaining black box algorithms: epistemological challenges and machine learning solutions. - David S. Watson (2021)

Models for Classifying AI Systems: the Switch, the Ladder, and the Matrix. - Jakob Mökander; Margi Sheth; David S. Watson; Luciano Floridi (FAccT, 2022)

Rational Shapley Values. - David S. Watson (FAccT, 2022)

Causal discovery under a confounder blanket. - David S. Watson; Ricardo Silva 001 (UAI, 2022)

Smooth densities and generative modeling with unsupervised random forests. - David S. Watson; Kristin Blesch; Jan Kapar; Marvin N. Wright (CoRR, 2022)

Conditional Feature Importance for Mixed Data. - Kristin Blesch; David S. Watson; Marvin N. Wright (CoRR, 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)

Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice. - David S. Watson; Limor Gultchin; Ankur Taly; Luciano Floridi (Minds Mach., 2022)

Adversarial Random Forests for Density Estimation and Generative Modeling. - David S. Watson; Kristin Blesch; Jan Kapar; Marvin N. Wright (AISTATS, 2023)

Stochastic Causal Programming for Bounding Treatment Effects. - Kirtan Padh; Jakob Zeitler; David S. Watson; Matt J. Kusner; Ricardo Silva 001; Niki Kilbertus (CLeaR, 2023)

Intervention Generalization: A View from Factor Graph Models. - Gecia Bravo Hermsdorff; David S. Watson; Jialin Yu; Jakob Zeitler; Ricardo Silva 001 (NeurIPS, 2023)

Explaining Predictive Uncertainty with Information Theoretic Shapley Values. - David S. Watson; Joshua O'Hara; Niek Tax; Richard Mudd; Ido Guy (NeurIPS, 2023)

Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values. - Kristin Blesch; Marvin N. Wright; David S. Watson (xAI (1), 2023)

The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems. - Jakob Mökander; Margi Sheth; David S. Watson; Luciano Floridi (Minds Mach., 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., 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., 2023)

Bounding causal effects with leaky instruments. - David S. Watson; Jordan Penn; Lee M. Gunderson; Gecia Bravo Hermsdorff; Afsaneh Mastouri; Ricardo Silva 001 (UAI, 2024)

A Genealogical Approach to Algorithmic Bias. - Marta Ziosi; David S. Watson; Luciano Floridi (Minds Mach., 2024)

BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments. - Jordan Penn; Lee M. Gunderson; Gecia Bravo Hermsdorff; Ricardo Silva 001; David S. Watson (AISTATS, 2025)

Hierarchical Fuzzy Model-Agnostic Explanation: Framework, Algorithms, and Interface for XAI. - Faliang Yin; Hak-Keung Lam; David S. Watson (IEEE Trans. Fuzzy Syst., 2025)

Autoencoding Random Forests. - Binh Duc Vu; Jan Kapar; Marvin N. Wright; David S. Watson (CoRR, 2025)

Competing narratives in AI ethics: a defense of sociotechnical pragmatism. - David S. Watson; Jakob Mökander; Luciano Floridi (AI Soc., 2025)

Missing value imputation with adversarial random forests - MissARF. - Pegah Golchian; Jan Kapar; David S. Watson; Marvin N. Wright (CoRR, 2025)