David Watson is doctoral candidate at the University of Oxford and an enrichment student at The Alan Turing Institute. He received his MSc from the Oxford Internet Institute in 2015, studying under the supervision of Professor Luciano Floridi. He went on to become a Data Scientist at Queen Mary University’s Centre for Translational Bioinformatics before returning to Oxford for his DPhil in 2017. He is passionate about promoting more interpretable techniques for analysing complex systems in the social and life sciences.
David is a founding member of the Digital Ethics Lab, where his research focuses on the epistemological foundations of machine learning. He develops new methods for explaining the outputs of black box algorithms, with the goal of better understanding causal relationships in high-dimensional systems. In addition to his academic work, David is a regular contributor to The Economist, where he writes articles and builds models for the Graphic Detail and Game Theory blogs.
- (2021) Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice.
- (2020) "M3C: Monte Carlo reference-based consensus clustering", Scientific Reports. 10 (1) 1816.
- (2020) Causal Feature Learning for Utility-Maximizing Agents.
- (2020) "Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci", Frontiers in Genetics. 11 350.
- (2020) "Spectrum: fast density-aware spectral clustering for single and multi-omic data", Bioinformatics. 36 (4) 1159-1166.
- (2019) "The Explanation Game: A Formal Framework for Interpretable Machine Learning", Synthese.
- (2019) "The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence", MINDS AND MACHINES. 29 (3) 417-440.
- (2019) "Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes: towards a new disease taxonomy and personalised medicine", Cell Reports. 28 (9) 2455-2470.
- (2019) "Molecular Portraits of Early Rheumatoid Arthritis Identify Clinical and Treatment Response Phenotypes", Cell Reports. 28 (9) 2455-2470.e5.
- (2019) "Clinical applications of machine learning algorithms: beyond the black box.", BMJ. 364 l886.
- (2019) Testing Conditional Independence in Supervised Learning Algorithms.
- (2019) "Are the dead taking over Facebook? A Big Data approach to the future of death online", Big Data and Society. 6 (1) 205395171984254.
- (2019) "Oncometabolite induced primary cilia loss in pheochromocytoma", Endocrine-Related Cancer. 26 (1) 165-180.
- (2019) "A Framework for Multi-Omic Prediction of Treatment Response to Biologic Therapy for Psoriasis", Journal of Investigative Dermatology. 139 (1) 100-107.
- (2018) "Correction: Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study", PLOS Medicine. 15 (10) e1002694.
- (2018) "Sex differences in the nitrate-nitrite-NO• pathway: Role of oral nitrate-reducing bacteria", Free Radical Biology and Medicine. 126 113-121.
- (2018) "Bioinformatics for dermatology – why we should learn about code", British Journal of Dermatology. 178 (4) 984.
- (2018) "The RA-MAP Consortium: a working model for academia-industry collaboration.", Nature reviews. Rheumatology. 14 (1) 53-60.
- (2017) "Research Techniques Made Simple: Bioinformatics for Genome-Scale Biology", Journal of Investigative Dermatology. 137 (9) e163-e168.
- (2017) "Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study", PLOS Medicine. 14 (7) e1002352.
- (2016) "Crowdsourced Science: Sociotechnical Epistemology in the e-Research Paradigm", Synthese. 195 (2) 741-764.