Dr David Watson
Lecturer in Artificial Intelligence
Research interests
- Computer science
Contact details
Biography
Dr David Watson is a Lecturer in the Department of Informatics. Before joining King’s, he was a Postdoctoral Research Fellow in the Department of Statistical Science at University College London. He obtained his doctorate from the University of Oxford.
Research interests
- Machine learning
- Causality
Further information
Bounding Causal Effects with Leaky Instruments
Watson, D. S., Penn, J., Gunderson, L. M., Bravo-Hermsdorff, G., Mastouri, A. & Silva, R., 2024, Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence. Vol. 244. p. 3689-3710 22 p. (Proceedings of Machine Learning Research).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Competing narratives in AI ethics: A defense of sociotechnical pragmatism
Watson, D. S., Mökander, J. & Floridi, L., 20 Dec 2024, (Accepted/In press) In: AI and Society. 100205.Research output: Contribution to journal › Article › peer-review
Bounding causal effects with leaky instruments
Watson, D., Bravo-Hermsdorff, G., Gunderson, L. M., Penn, J., Mastouri, A. & Silva, R., Jul 2024, Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments
Penn, J., Gunderson, L. M., Bravo-Hermsdorff, G., Silva, R. & Watson, D. S., 11 Nov 2024.Research output: Working paper/Preprint › Preprint
Intervention Generalization: A View from Factor Graph Models
Bravo-Hermsdorff, G., Watson, D. S., Yu, J., Zeitler, J. & Silva, R., 2023, Advances in Neural Information Processing Systems. Vol. 36. p. 43662-43675 14 p. (ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values
Blesch, K., Wright, M. N. & Watson, D., 2023, In: Communications in Computer and Information Science. p. 131-146 16 p.Research output: Contribution to journal › Article › peer-review
Hierarchical fuzzy model-agnostic explanation: framework, algorithms and interface for XAI
Yin, F., Lam, H.-K. & Watson, D., 18 Oct 2024, (Accepted/In press) In: IEEE Transactions on Fuzzy Systems.Research output: Contribution to journal › Article › peer-review
A Genealogical Approach to Algorithmic Bias
Ziosi, M., Watson, D. & Floridi, L., Jun 2024, In: Minds and Machines. 34, 2, 9.Research output: Contribution to journal › Article › peer-review
Intervention Generalization: A View from Factor Graph Models
Bravo-Hermsdorff, G., Watson, D., Yu, J., Zeitler, J. & Silva, R., 1 Dec 2023, Advances in Neural Information Processing Systems. Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M. & Levine, S. (eds.). Curran Associates Inc., Vol. 36. p. 43662-43675 14 p.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Explaining Predictive Uncertainty with Information Theoretic Shapley Values
Watson, D. S., Tax, N., O'Hara, J., Mudd, R. & Guy, I., 1 Dec 2023, Advances in Neural Information Processing Systems. Vol. 36. (ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values
Watson, D., 30 Oct 2023, World Conference on Explainable Artificial Intelligence.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Reply to Tom Sterkenburg’s Commentary
Watson, D. S., Dec 2023, In: Philosophy and Technology. 36, 4, 69.Research output: Contribution to journal › Comment/debate › peer-review
Stochastic Causal Programming for Bounding Treatment Effects
Padh, K., Zeitler, J., Watson, D., Kusner, M., Silva, R. & Kilbertus, N., 2023, Proceedings of The 2nd Conference on Causal Learning and Reasoning. Vol. 213. p. 142-176 35 p. (Proceedings of Machine Learning Research).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Correction to: The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems (Minds and Machines, (2023), 33, 1, (221-248), 10.1007/s11023-022-09620-y)
Mökander, J., Sheth, M., Watson, D. S. & Floridi, L., Mar 2023, In: MINDS AND MACHINES. 33, 1, p. 249 1 p.Research output: Contribution to journal › Comment/debate › peer-review
The Ethics of Online Controlled Experiments (A/B Testing)
Polonioli, A., Ghioni, R., Greco, C., Juneja, P., Tagliabue, J., Watson, D. & Floridi, L., 2023, (Accepted/In press) In: MINDS AND MACHINES.Research output: Contribution to journal › Article › peer-review
The benefits and pitfalls of machine learning for biomarker discovery
Ng, S., Masarone, S., Watson, D. & Barnes, M. R., Oct 2023, In: Cell and Tissue Research. 394, 1, p. 17-31 15 p.Research output: Contribution to journal › Review article › peer-review
Adversarial Random Forests for Density Estimation and Generative Modeling
Watson, D. S., Blesch, K., Kapar, J. & Wright, M. N., 2023, Proceedings of the 26th Conference on Artificial Intelligence and Statistics. Vol. 206. p. 5357-5375 19 p. (Proceedings of Machine Learning Research).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Conditional feature importance for mixed data
Blesch, K., Watson, D. & Wright, M., 29 Apr 2023, In: AStA Advances in Statistical Analysis.Research output: Contribution to journal › Article › peer-review
On the philosophy of unsupervised learning
Watson, D., 21 Apr 2023, In: Philosophy and Technology. 36, 2, 28.Research output: Contribution to journal › Article › peer-review
The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems
Mökander, J., Sheth, M., Watson, D. S. & Floridi, L., Mar 2023, In: MINDS AND MACHINES. 33, 1, p. 221-248 28 p.Research output: Contribution to journal › Article › peer-review
The epistemological foundations of data science: a critical review
Desai, J., Watson, D., Wang, V., Taddeo, M. & Floridi, L., 8 Nov 2022, In: SYNTHESE. 200, 6, 469 .Research output: Contribution to journal › Article › peer-review
Operationalizing complex causes: A pragmatic view of mediation
Gultchin, L., Watson, D., Kusner, M. & Silva, R., Jul 2021, Proceedings of the 38th International Conference on Machine Learning. Vol. 139. p. 3875-3885Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Causal feature learning for utility-maximizing agents
Kinney, D. & Watson, D., Sept 2020, Proceedings of the 10th International Conference on Probabilistic Graphical Models. Vol. 138. p. 357-368Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Causal discovery under a confounder blanket
Watson, D. & Silva, R., Aug 2022, Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence. Vol. 180. p. 2096-2106Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Rational Shapley Values
Watson, D., 21 Jun 2022, Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022. p. 1083-1094 12 p. (ACM International Conference Proceeding Series).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
No explanation without inference
Watson, D. S., 2021, AISB Convention 2021: Communication and Conversations. The Society for the Study of Artificial Intelligence and Simulation of Behaviour, (AISB Convention 2021: Communication and Conversations).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study
Cabrera, C. P., Manson, J., Shepherd, J. M., Torrance, H. D., Watson, D., Longhi, M. P., Hoti, M., Patel, M. B., O’Dwyer, M., Nourshargh, S., Pennington, D. J., Barnes, M. R. & Brohi, K., Jul 2017, In: PLoS Medicine. 14, 7, e1002352.Research output: Contribution to journal › Article › peer-review
Research Techniques Made Simple: Bioinformatics for Genome-Scale Biology
Foulkes, A. C., Watson, D. S., Griffiths, C. E. M., Warren, R. B., Huber, W. & Barnes, M. R., Sept 2017, In: Journal of Investigative Dermatology. 137, 9, p. e163-e168Research output: Contribution to journal › Short survey › peer-review
Crowdsourced science: sociotechnical epistemology in the e-research paradigm
Watson, D. & Floridi, L., 1 Feb 2018, In: SYNTHESE. 195, 2, p. 741-764 24 p.Research output: Contribution to journal › Article › peer-review
Bioinformatics for dermatology: why we should learn about code
Foulkes, A. C., Watson, D. S. & Barnes, M. R., Apr 2018, In: British Journal of Dermatology. 178, 4, p. 984 1 p.Research output: Contribution to journal › Letter › peer-review
Erratum: Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study (PLoS Med (2017) 14:7 (e1002352) 10.1371/journal.pmed.1002352)
Cabrera, C. P., Manson, J., Shepherd, J. M., Torrance, H. D., Watson, D., Paula Longhi, M., Hoti, M., Patel, M. B., O'Dwyer, M., Nourshargh, S., Pennington, D. J., Barnes, M. R. & Brohi, K., Oct 2018, In: PLoS Medicine. 15, 10, e1002694.Research output: Contribution to journal › Comment/debate › peer-review
A Framework for Multi-Omic Prediction of Treatment Response to Biologic Therapy for Psoriasis
the PSORT Consortium, Jan 2019, In: Journal of Investigative Dermatology. 139, 1, p. 100-107 8 p.Research output: Contribution to journal › Article › peer-review
Oncometabolite induced primary cilia loss in pheochromocytoma
O’Toole, S. M., Watson, D. S., Novoselova, T. V., Romano, L. E. L., King, P. J., Bradshaw, T. Y., Thompson, C. L., Knight, M. M., Sharp, T. V., Barnes, M. R., Srirangalingam, U., Drake, W. M. & Chapple, J. P., Jan 2019, In: Endocrine-Related Cancer. 26, 1, p. 165-180 16 p.Research output: Contribution to journal › Article › peer-review
Clinical applications of machine learning algorithms: Beyond the black box
Watson, D. S., Krutzinna, J., Bruce, I. N., Griffiths, C. E. M., McInnes, I. B., Barnes, M. R. & Floridi, L., 2019, In: BMJ (Online). 364, ll886.Research output: Contribution to journal › Article › peer-review
Molecular Portraits of Early Rheumatoid Arthritis Identify Clinical and Treatment Response Phenotypes
Lewis, M. J., Barnes, M. R., Blighe, K., Goldmann, K., Rana, S., Hackney, J. A., Ramamoorthi, N., John, C. R., Watson, D. S., Kummerfeld, S. K., Hands, R., Riahi, S., Rocher-Ros, V., Rivellese, F., Humby, F., Kelly, S., Bombardieri, M., Ng, N., DiCicco, M. & van der Heijde, D. & 9 others, Landewé, R., van der Helm-van Mil, A., Cauli, A., McInnes, I. B., Buckley, C. D., Choy, E., Taylor, P. C., Townsend, M. J. & Pitzalis, C., 27 Aug 2019, In: Cell Reports. 28, 9, p. 2455-2470.e5Research output: Contribution to journal › Article › peer-review
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence
Watson, D., 1 Sept 2019, In: MINDS AND MACHINES. 29, 3, p. 417-440 24 p.Research output: Contribution to journal › Article › peer-review
Are the dead taking over Facebook? A Big Data approach to the future of death online
Öhman, C. J. & Watson, D., Jan 2019, In: Big Data and Society. 6, 1Research output: Contribution to journal › Article › peer-review
M3C: Monte Carlo reference-based consensus clustering
John, C. R., Watson, D., Russ, D., Goldmann, K., Ehrenstein, M., Pitzalis, C., Lewis, M. & Barnes, M., 1 Dec 2020, In: Scientific Reports. 10, 1, 1816.Research output: Contribution to journal › Article › peer-review
Spectrum: Fast density-aware spectral clustering for single and multi-omic data
John, C. R., Watson, D., Barnes, M. R., Pitzalis, C. & Lewis, M. J., 15 Feb 2020, In: BIOINFORMATICS. 36, 4, p. 1159-1166 8 p.Research output: Contribution to journal › Article › peer-review
Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci
Nicholls, H. L., John, C. R., Watson, D. S., Munroe, P. B., Barnes, M. R. & Cabrera, C. P., 15 Apr 2020, In: Frontiers in Genetics. 11, 350.Research output: Contribution to journal › Review article › peer-review
The explanation game: a formal framework for interpretable machine learning
Watson, D. S. & Floridi, L., Oct 2021, In: SYNTHESE. 198, 10, p. 9211-9242 32 p.Research output: Contribution to journal › Article › peer-review
Testing conditional independence in supervised learning algorithms
Watson, D. S. & Wright, M. N., Aug 2021, In: MACHINE LEARNING. 110, 8, p. 2107-2129 23 p.Research output: Contribution to journal › Article › peer-review
The paradox of poor representation: How voter–party incongruence curbs affective polarisation
Marchal, N. & Watson, D. S., Nov 2022, In: British Journal of Politics and International Relations. 24, 4, p. 668-685 18 p.Research output: Contribution to journal › Article › peer-review
The Explanation Game: A Formal Framework for Interpretable Machine Learning
Watson, D. S. & Floridi, L., 2021, Philosophical Studies Series. Springer Nature, p. 185-219 35 p. (Philosophical Studies Series; vol. 144).Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
Watson, D. S., Gultchin, L., Taly, A. & Floridi, L., 2021, p. 1382-1392. 11 p.Research output: Contribution to conference types › Paper › peer-review
Interpretable machine learning for genomics
Watson, D. S., Sept 2022, In: Human Genetics. 141, 9, p. 1499-1513 15 p.Research output: Contribution to journal › Article › peer-review
Conceptual challenges for interpretable machine learning
Watson, D. S., Feb 2022, In: SYNTHESE. 200, 1Research output: Contribution to journal › Article › peer-review
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
Watson, D. S., Gultchin, L., Taly, A. & Floridi, L., Mar 2022, In: MINDS AND MACHINES. 32, 1, p. 185-218 34 p.Research output: Contribution to journal › Article › peer-review
The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other?
Mökander, J., Juneja, P., Watson, D. S. & Floridi, L., Dec 2022, In: MINDS AND MACHINES. 32, 4, p. 751-758 8 p.Research output: Contribution to journal › Article › peer-review
RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients
The RA-MAP Consortium, Isaacs, J. D., Brockbank, S., Pedersen, A. W., Hilkens, C., Anderson, A., Stocks, P., Lendrem, D., Tarn, J., Smith, G. R., Allen, B., Casement, J., Diboll, J., Harry, R., Cooles, F. A. H., Cope, A. P., Simpson, G., Toward, R., Noble, H. & Parke, A. & 31 others, Wu, W., Clarke, F., Scott, D., Scott, I. C., Galloway, J., Lempp, H., Ibrahim, F., Schwank, S., Molyneux, G., Lazarov, T., Geissmann, F., Goodyear, C. S., McInnes, I. B., Donnelly, I., Gilmour, A., Virlan, A. T., Porter, D., Ponchel, F., Emery, P., El-Jawhari, J., Parmar, R., McDermott, M. F., Fisher, B. A., Young, S. P., Jones, P., Raza, K., Filer, A., Pitzalis, C., Barnes, M. R., Watson, D. S. & Henkin, R., Dec 2022, In: Scientific Data. 9, 1, 196.Research output: Contribution to journal › Article › peer-review
Research
Health Hub
The Health Hub centres on computational characterisation of medically relevant study cases and data.
Distributed Artificial Intelligence
Understanding AI in social and economic contexts where an intelligent entity may be interacting with other entities
News
Scientists scoop prestigious New Investigator Awards
The grants will help accelerate the development of trusted AI models, advance our understanding of the mathematics of chaos and accelerate the Net Zero...
Events
Population Health Seminar with David Watson
Seminar with David Watson
Please note: this event has passed.
Features
AI Pragmatism: The only way to growth (without the AI arms race)
The battle between Silicon Valley techno-utopians and AI sceptics abounds with society caught in the middle. Larger and larger AI models are unleashed on the...
Meet our new researchers from the Department of Informatics
We interview some of our academics who started September 2022.
Bounding Causal Effects with Leaky Instruments
Watson, D. S., Penn, J., Gunderson, L. M., Bravo-Hermsdorff, G., Mastouri, A. & Silva, R., 2024, Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence. Vol. 244. p. 3689-3710 22 p. (Proceedings of Machine Learning Research).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Competing narratives in AI ethics: A defense of sociotechnical pragmatism
Watson, D. S., Mökander, J. & Floridi, L., 20 Dec 2024, (Accepted/In press) In: AI and Society. 100205.Research output: Contribution to journal › Article › peer-review
Bounding causal effects with leaky instruments
Watson, D., Bravo-Hermsdorff, G., Gunderson, L. M., Penn, J., Mastouri, A. & Silva, R., Jul 2024, Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
BudgetIV: Optimal Partial Identification of Causal Effects with Mostly Invalid Instruments
Penn, J., Gunderson, L. M., Bravo-Hermsdorff, G., Silva, R. & Watson, D. S., 11 Nov 2024.Research output: Working paper/Preprint › Preprint
Intervention Generalization: A View from Factor Graph Models
Bravo-Hermsdorff, G., Watson, D. S., Yu, J., Zeitler, J. & Silva, R., 2023, Advances in Neural Information Processing Systems. Vol. 36. p. 43662-43675 14 p. (ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values
Blesch, K., Wright, M. N. & Watson, D., 2023, In: Communications in Computer and Information Science. p. 131-146 16 p.Research output: Contribution to journal › Article › peer-review
Hierarchical fuzzy model-agnostic explanation: framework, algorithms and interface for XAI
Yin, F., Lam, H.-K. & Watson, D., 18 Oct 2024, (Accepted/In press) In: IEEE Transactions on Fuzzy Systems.Research output: Contribution to journal › Article › peer-review
A Genealogical Approach to Algorithmic Bias
Ziosi, M., Watson, D. & Floridi, L., Jun 2024, In: Minds and Machines. 34, 2, 9.Research output: Contribution to journal › Article › peer-review
Intervention Generalization: A View from Factor Graph Models
Bravo-Hermsdorff, G., Watson, D., Yu, J., Zeitler, J. & Silva, R., 1 Dec 2023, Advances in Neural Information Processing Systems. Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M. & Levine, S. (eds.). Curran Associates Inc., Vol. 36. p. 43662-43675 14 p.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Explaining Predictive Uncertainty with Information Theoretic Shapley Values
Watson, D. S., Tax, N., O'Hara, J., Mudd, R. & Guy, I., 1 Dec 2023, Advances in Neural Information Processing Systems. Vol. 36. (ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Unfooling SHAP and SAGE: Knockoff Imputation for Shapley Values
Watson, D., 30 Oct 2023, World Conference on Explainable Artificial Intelligence.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Reply to Tom Sterkenburg’s Commentary
Watson, D. S., Dec 2023, In: Philosophy and Technology. 36, 4, 69.Research output: Contribution to journal › Comment/debate › peer-review
Stochastic Causal Programming for Bounding Treatment Effects
Padh, K., Zeitler, J., Watson, D., Kusner, M., Silva, R. & Kilbertus, N., 2023, Proceedings of The 2nd Conference on Causal Learning and Reasoning. Vol. 213. p. 142-176 35 p. (Proceedings of Machine Learning Research).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Correction to: The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems (Minds and Machines, (2023), 33, 1, (221-248), 10.1007/s11023-022-09620-y)
Mökander, J., Sheth, M., Watson, D. S. & Floridi, L., Mar 2023, In: MINDS AND MACHINES. 33, 1, p. 249 1 p.Research output: Contribution to journal › Comment/debate › peer-review
The Ethics of Online Controlled Experiments (A/B Testing)
Polonioli, A., Ghioni, R., Greco, C., Juneja, P., Tagliabue, J., Watson, D. & Floridi, L., 2023, (Accepted/In press) In: MINDS AND MACHINES.Research output: Contribution to journal › Article › peer-review
The benefits and pitfalls of machine learning for biomarker discovery
Ng, S., Masarone, S., Watson, D. & Barnes, M. R., Oct 2023, In: Cell and Tissue Research. 394, 1, p. 17-31 15 p.Research output: Contribution to journal › Review article › peer-review
Adversarial Random Forests for Density Estimation and Generative Modeling
Watson, D. S., Blesch, K., Kapar, J. & Wright, M. N., 2023, Proceedings of the 26th Conference on Artificial Intelligence and Statistics. Vol. 206. p. 5357-5375 19 p. (Proceedings of Machine Learning Research).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Conditional feature importance for mixed data
Blesch, K., Watson, D. & Wright, M., 29 Apr 2023, In: AStA Advances in Statistical Analysis.Research output: Contribution to journal › Article › peer-review
On the philosophy of unsupervised learning
Watson, D., 21 Apr 2023, In: Philosophy and Technology. 36, 2, 28.Research output: Contribution to journal › Article › peer-review
The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems
Mökander, J., Sheth, M., Watson, D. S. & Floridi, L., Mar 2023, In: MINDS AND MACHINES. 33, 1, p. 221-248 28 p.Research output: Contribution to journal › Article › peer-review
The epistemological foundations of data science: a critical review
Desai, J., Watson, D., Wang, V., Taddeo, M. & Floridi, L., 8 Nov 2022, In: SYNTHESE. 200, 6, 469 .Research output: Contribution to journal › Article › peer-review
Operationalizing complex causes: A pragmatic view of mediation
Gultchin, L., Watson, D., Kusner, M. & Silva, R., Jul 2021, Proceedings of the 38th International Conference on Machine Learning. Vol. 139. p. 3875-3885Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Causal feature learning for utility-maximizing agents
Kinney, D. & Watson, D., Sept 2020, Proceedings of the 10th International Conference on Probabilistic Graphical Models. Vol. 138. p. 357-368Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Causal discovery under a confounder blanket
Watson, D. & Silva, R., Aug 2022, Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence. Vol. 180. p. 2096-2106Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Rational Shapley Values
Watson, D., 21 Jun 2022, Proceedings of 2022 5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022. p. 1083-1094 12 p. (ACM International Conference Proceeding Series).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
No explanation without inference
Watson, D. S., 2021, AISB Convention 2021: Communication and Conversations. The Society for the Study of Artificial Intelligence and Simulation of Behaviour, (AISB Convention 2021: Communication and Conversations).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study
Cabrera, C. P., Manson, J., Shepherd, J. M., Torrance, H. D., Watson, D., Longhi, M. P., Hoti, M., Patel, M. B., O’Dwyer, M., Nourshargh, S., Pennington, D. J., Barnes, M. R. & Brohi, K., Jul 2017, In: PLoS Medicine. 14, 7, e1002352.Research output: Contribution to journal › Article › peer-review
Research Techniques Made Simple: Bioinformatics for Genome-Scale Biology
Foulkes, A. C., Watson, D. S., Griffiths, C. E. M., Warren, R. B., Huber, W. & Barnes, M. R., Sept 2017, In: Journal of Investigative Dermatology. 137, 9, p. e163-e168Research output: Contribution to journal › Short survey › peer-review
Crowdsourced science: sociotechnical epistemology in the e-research paradigm
Watson, D. & Floridi, L., 1 Feb 2018, In: SYNTHESE. 195, 2, p. 741-764 24 p.Research output: Contribution to journal › Article › peer-review
Bioinformatics for dermatology: why we should learn about code
Foulkes, A. C., Watson, D. S. & Barnes, M. R., Apr 2018, In: British Journal of Dermatology. 178, 4, p. 984 1 p.Research output: Contribution to journal › Letter › peer-review
Erratum: Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study (PLoS Med (2017) 14:7 (e1002352) 10.1371/journal.pmed.1002352)
Cabrera, C. P., Manson, J., Shepherd, J. M., Torrance, H. D., Watson, D., Paula Longhi, M., Hoti, M., Patel, M. B., O'Dwyer, M., Nourshargh, S., Pennington, D. J., Barnes, M. R. & Brohi, K., Oct 2018, In: PLoS Medicine. 15, 10, e1002694.Research output: Contribution to journal › Comment/debate › peer-review
A Framework for Multi-Omic Prediction of Treatment Response to Biologic Therapy for Psoriasis
the PSORT Consortium, Jan 2019, In: Journal of Investigative Dermatology. 139, 1, p. 100-107 8 p.Research output: Contribution to journal › Article › peer-review
Oncometabolite induced primary cilia loss in pheochromocytoma
O’Toole, S. M., Watson, D. S., Novoselova, T. V., Romano, L. E. L., King, P. J., Bradshaw, T. Y., Thompson, C. L., Knight, M. M., Sharp, T. V., Barnes, M. R., Srirangalingam, U., Drake, W. M. & Chapple, J. P., Jan 2019, In: Endocrine-Related Cancer. 26, 1, p. 165-180 16 p.Research output: Contribution to journal › Article › peer-review
Clinical applications of machine learning algorithms: Beyond the black box
Watson, D. S., Krutzinna, J., Bruce, I. N., Griffiths, C. E. M., McInnes, I. B., Barnes, M. R. & Floridi, L., 2019, In: BMJ (Online). 364, ll886.Research output: Contribution to journal › Article › peer-review
Molecular Portraits of Early Rheumatoid Arthritis Identify Clinical and Treatment Response Phenotypes
Lewis, M. J., Barnes, M. R., Blighe, K., Goldmann, K., Rana, S., Hackney, J. A., Ramamoorthi, N., John, C. R., Watson, D. S., Kummerfeld, S. K., Hands, R., Riahi, S., Rocher-Ros, V., Rivellese, F., Humby, F., Kelly, S., Bombardieri, M., Ng, N., DiCicco, M. & van der Heijde, D. & 9 others, Landewé, R., van der Helm-van Mil, A., Cauli, A., McInnes, I. B., Buckley, C. D., Choy, E., Taylor, P. C., Townsend, M. J. & Pitzalis, C., 27 Aug 2019, In: Cell Reports. 28, 9, p. 2455-2470.e5Research output: Contribution to journal › Article › peer-review
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence
Watson, D., 1 Sept 2019, In: MINDS AND MACHINES. 29, 3, p. 417-440 24 p.Research output: Contribution to journal › Article › peer-review
Are the dead taking over Facebook? A Big Data approach to the future of death online
Öhman, C. J. & Watson, D., Jan 2019, In: Big Data and Society. 6, 1Research output: Contribution to journal › Article › peer-review
M3C: Monte Carlo reference-based consensus clustering
John, C. R., Watson, D., Russ, D., Goldmann, K., Ehrenstein, M., Pitzalis, C., Lewis, M. & Barnes, M., 1 Dec 2020, In: Scientific Reports. 10, 1, 1816.Research output: Contribution to journal › Article › peer-review
Spectrum: Fast density-aware spectral clustering for single and multi-omic data
John, C. R., Watson, D., Barnes, M. R., Pitzalis, C. & Lewis, M. J., 15 Feb 2020, In: BIOINFORMATICS. 36, 4, p. 1159-1166 8 p.Research output: Contribution to journal › Article › peer-review
Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci
Nicholls, H. L., John, C. R., Watson, D. S., Munroe, P. B., Barnes, M. R. & Cabrera, C. P., 15 Apr 2020, In: Frontiers in Genetics. 11, 350.Research output: Contribution to journal › Review article › peer-review
The explanation game: a formal framework for interpretable machine learning
Watson, D. S. & Floridi, L., Oct 2021, In: SYNTHESE. 198, 10, p. 9211-9242 32 p.Research output: Contribution to journal › Article › peer-review
Testing conditional independence in supervised learning algorithms
Watson, D. S. & Wright, M. N., Aug 2021, In: MACHINE LEARNING. 110, 8, p. 2107-2129 23 p.Research output: Contribution to journal › Article › peer-review
The paradox of poor representation: How voter–party incongruence curbs affective polarisation
Marchal, N. & Watson, D. S., Nov 2022, In: British Journal of Politics and International Relations. 24, 4, p. 668-685 18 p.Research output: Contribution to journal › Article › peer-review
The Explanation Game: A Formal Framework for Interpretable Machine Learning
Watson, D. S. & Floridi, L., 2021, Philosophical Studies Series. Springer Nature, p. 185-219 35 p. (Philosophical Studies Series; vol. 144).Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
Watson, D. S., Gultchin, L., Taly, A. & Floridi, L., 2021, p. 1382-1392. 11 p.Research output: Contribution to conference types › Paper › peer-review
Interpretable machine learning for genomics
Watson, D. S., Sept 2022, In: Human Genetics. 141, 9, p. 1499-1513 15 p.Research output: Contribution to journal › Article › peer-review
Conceptual challenges for interpretable machine learning
Watson, D. S., Feb 2022, In: SYNTHESE. 200, 1Research output: Contribution to journal › Article › peer-review
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
Watson, D. S., Gultchin, L., Taly, A. & Floridi, L., Mar 2022, In: MINDS AND MACHINES. 32, 1, p. 185-218 34 p.Research output: Contribution to journal › Article › peer-review
The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other?
Mökander, J., Juneja, P., Watson, D. S. & Floridi, L., Dec 2022, In: MINDS AND MACHINES. 32, 4, p. 751-758 8 p.Research output: Contribution to journal › Article › peer-review
RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients
The RA-MAP Consortium, Isaacs, J. D., Brockbank, S., Pedersen, A. W., Hilkens, C., Anderson, A., Stocks, P., Lendrem, D., Tarn, J., Smith, G. R., Allen, B., Casement, J., Diboll, J., Harry, R., Cooles, F. A. H., Cope, A. P., Simpson, G., Toward, R., Noble, H. & Parke, A. & 31 others, Wu, W., Clarke, F., Scott, D., Scott, I. C., Galloway, J., Lempp, H., Ibrahim, F., Schwank, S., Molyneux, G., Lazarov, T., Geissmann, F., Goodyear, C. S., McInnes, I. B., Donnelly, I., Gilmour, A., Virlan, A. T., Porter, D., Ponchel, F., Emery, P., El-Jawhari, J., Parmar, R., McDermott, M. F., Fisher, B. A., Young, S. P., Jones, P., Raza, K., Filer, A., Pitzalis, C., Barnes, M. R., Watson, D. S. & Henkin, R., Dec 2022, In: Scientific Data. 9, 1, 196.Research output: Contribution to journal › Article › peer-review
Research
Health Hub
The Health Hub centres on computational characterisation of medically relevant study cases and data.
Distributed Artificial Intelligence
Understanding AI in social and economic contexts where an intelligent entity may be interacting with other entities
News
Scientists scoop prestigious New Investigator Awards
The grants will help accelerate the development of trusted AI models, advance our understanding of the mathematics of chaos and accelerate the Net Zero...
Events
Population Health Seminar with David Watson
Seminar with David Watson
Please note: this event has passed.
Features
AI Pragmatism: The only way to growth (without the AI arms race)
The battle between Silicon Valley techno-utopians and AI sceptics abounds with society caught in the middle. Larger and larger AI models are unleashed on the...
Meet our new researchers from the Department of Informatics
We interview some of our academics who started September 2022.