Chamberlain J (2019) Exploring the potential for automation and artificial intelligence in the regulation of the health and social care professions in the United Kingdom. Welcome Trust. https://cronfa.swan.ac.uk/Record/cronfa48862
This horizon scanning Wellcome Trust funded project was tasked with exploring the impact of current advances in computing and information processing, in the field of professional regulation in the United Kingdom. Although key advances in mathematics, information processing, machine learning, automation and artificial intelligence are beginning to disrupt and transform traditional practices in health and social care in the United Kingdom, the project found that the same cannot be said in relation to the field of professional regulation. At present, the focus of the regulatory reform agenda has been on promoting a more joined-up, risk-adverse and public-interest focused model of ‘right touch’ regulation. However, the project concluded that this agenda will not by itself enable regulators to embed current and future developments in automation and machine learning within their organisational structures. The fractured and decontextualized nature of the current regulatory data lake means that despite their recent efforts to develop their respective intelligence and insight agendas to improve the predictive risk templates used to identify threats to public safety, at present regulators possess a very low level of readiness in relation to the information capture and analysis systems required by algorithmic digital technologies. It is the key recommendation of the project that action be taken to standardize current regulatory data warehouse information capture and processing systems, with a view to support the development of a shared data lake between regulators. Furthermore, this warehouse should be curated by an independent statutory body, such as the Professional Standards Authority, to meet public-interest expectations and GDPR requirements, particularly in relation to the future development of regulatory predictive risk-templates.
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