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Abstract counterfactuals for language model agents. arXiv preprint, 2506.02946.
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Autoencoding random forests. arXiv preprint, 2505.21441.
Penn et al.
BudgetIV: Optimal partial identification of causal effects with mostly invalid instruments. Accepted to The 28th International Conference on Artificial Intelligence and Statistics.
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Competing narratives in AI ethics: A defense of sociotechnical pragmatism. AI & Society.
Yin et al.
Hierarchical fuzzy model-agnostic explanations. IEEE Transactions on Fuzzy Systems.
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Bounding causal effects with leaky instruments. Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence.
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Explaining predictive uncertainty with information theoretic Shapley values. Advances in Neural Information Processing Systems 36, pp. 7330-7350.
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Intervention generalization: A view from factor graph models. Advances in Neural Information Processing Systems 36, pp. 43662-43675.
Polonioli et al.
The ethics of online controlled experiments (A/B testing). Minds & Machines, 33, 667-693.
Watson
On the philosophy of unsupervised learning. Philosophy & Technology, 36(28).
Watson et al.
Adversarial random forests for density estimation and generative modeling. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, pp. 5357-5375.
Padh et al.
Stochastic causal programming for bounding treatment effects. Proceedings of The 2nd Conference on Causal Learning and Reasoning, pp. 142-176.
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The switch, the ladder, and the matrix: Models for classifying AI systems. Minds & Machines, 33, 221-248.
Desai et al.
The epistemological foundations of data science: A critical analysis. Synthese, 200(6), 469.
Watson & Silva
Causal discovery under a confounder blanket. Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, pp. 2096-2106.
The RA-MAP Consortium
RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients. Scientific Data, 9(196).
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Rational Shapley values. Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 1083-1094.
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Conceptual challenges for interpretable machine learning. Synthese, 200(2), 65-98.
Watson
Interpretable machine learning for genomics. Human Genetics, 141(9), 1499-1513.
Marchal & Watson
The paradox of poor representation: How voter–party incongruence curbs affective polarisation. The British Journal of Politics and International Relations, 24(4), 668-685.
Watson & Wright
Testing conditional independence in supervised learning algorithms. Machine Learning, 110(8), 2107-2129.
Gultchin et al.
Operationalizing complex causes: A pragmatic view of mediation. Proceedings of the 38th International Conference on Machine Learning, pp. 3875-3885.
Watson
Explaining black box algorithms: Epistemological challenges and machine learning solutions. Doctoral Dissertation, University of Oxford.
Kinney & Watson
Causal feature learning for utility-maximizing agents. Proceedings of the 10th International Conference on Probabilistic Graphical Models, pp. 257-268.
Nicholls et al.
Reaching the end-game for GWAS: Machine learning approaches for the prioritization of complex disease loci. Frontiers in Genetics, 11, 350.
Watson & Floridi
The explanation game: A formal framework for interpretable machine learning. Synthese, 198(10), 9211-9242.
John et al.
M3C: Monte Carlo reference based consensus clustering. Scientific Reports, 10(1), 1816.
John et al.
Spectrum: Fast density-aware spectral clustering for single and multi-omic data. Bioinformatics, 36(4), 1159-1166.
Watson
The rhetoric and reality of anthropomorphism in artificial intelligence. Minds & Machines, 29(3), 414-440.
Öhman & Watson
Are the dead taking over facebook? A big data approach to the future of death online. Big Data & Society, 6(1), 1-13.
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Oncometabolite induced primary cilia loss in pheochromocytoma. Endocrine-Related Cancer, 26(1), 165-180.
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Clinical applications of machine learning algorithms: Beyond the black box. BMJ, 346, 446-448.
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The price of discovery: A model of scientific research markets. In Öhman, C. & Watson, D. (Eds), The 2018 Yearbook of the Digital Ethics Lab, pp. 51-63.
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A framework for multi-omic prediction of treatment response to biologic therapy for psoriasis. Journal of Investigative Dermatology, 139(1), 100-107.
Watson & Floridi
Crowdsourced science: Sociotechnical epistemology in the e-research paradigm. Synthese, 195(2), 741-764.