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| Principal Investigator | Professor Susan Michie, UCL |
| Co-Investigators | Professor John Shawe-Taylor (UCL), Professor James Thomas (UCL), Professor Mike Kelly (NICE), Professor Marie Johnston (University of Aberdeen), Pol Mac Aonghusa (IBM Research Lab, Dublin), Professor Robert West (UCL) |
| Additional UCL team members | Dr Ailbhe Finnerty, Dr Marta Marques, Dr Emma Norris, Candice Moore. Silje Zink, Emily Haynes (Researchers), Dr Alison Wright (Senior Researcher), Becca Jones (Project Manager), |
| Project start and end dates | September 2016 - August 2020 |
| Funder | Wellcome Trust - Collaborative Award in Science |
| Project website | www.humanbehaviourchange.org |
Project details
The Human Behaviour Change Project (HBCP) will build an Artificial Intelligence system to continually scan the world literature on behaviour change, extract key information, and use this to build and update a model of human behaviour to answer the big question: ‘What behaviour change interventions work, how well, for whom, in what setting, for what behaviours and why?’
A multi-disciplinary team of 12 will work over four years to revolutionise current practices of evidence synthesis and our ability to generate new knowledge. The work will depend on a close interplay between behavioural, computer and information science.
The behavioural scientists will develop an “ontology” of behaviour change interventions that will organise the fragmented knowledge in the scientific literature into a form that enables the efficient accumulation of knowledge.
The computer scientists will build an Artificial Intelligence system, trained by behavioural scientists, to apply Natural Language Processing to extract relevant information from scientific reports and to organise that information into the Ontology using reasoning and machine learning. The Artificial Intelligence system will furthermore infer new knowledge while continually learning from new information fed into it.
The information scientists will build and evaluate a sophisticated online user interface to interact with the Artificial Intelligence system to enable users to readily access the breadth and depth of up-to-date evidence, and get answers to their questions, with explanations of these answers that people can understand and trust.