Artificial Intelligence In The Public Sector – A New Regulatory Field?

The Committee on Standards in Public Life (the "Committee") has published a report on artificial intelligence ("AI") and its impact on public standards following the Committee's review into this fast-developing field (the "Report"). The Report sets out the Committee's recommendations for the governance and regulation of AI in the public sector, aimed at ensuring high standards of conduct across all areas of public sector practice.

The Report comes at a time when public bodies continue to increasingly seek to adopt technologically assisted and data-driven decision-making in varied sectors. This trend looks set to continue, albeit with many potential uses of AI in the public sector still at the development or 'proof-of-concept' stage. Although work on the ethical use of data is already being carried out by various organisations such as The Alan Turing Institute, the Centre for Data Ethics and Innovation (CDEI) and the Information Commissioner's Office, the Report identifies "significant deficiencies" in the UK's existing regulatory and governance framework for AI in the public sector and a pressing need for practical guidance and enforceable regulatory standards.

The recommendations in the Report, addressed in more detail below, suggest broad, overarching changes to the regulatory and governance framework and environment that will need buy-in from government if they are to result in a tangible impact on the approach to AI across the public sector. In addition to these systemic proposals, several of the recommendations of the Committee are directed at providers of public services, both public and private. These relate to both the planning stages of projects involving the use of AI and also the implementation stages, including in relation to monitoring and evaluation and also appeal and redress routes that are available to individuals impacted by automated and AI-assisted decisions.

What exactly counts as AI?

As the Report recognises, there is not one, universally accepted definition of what counts as AI. The term can be used to describe a broad range of processes, from simple automated data analysis to complex deep neural networks. Machine learning is an important subset of AI - machine learning systems are trained on existing datasets and identify patterns in the data. The systems employ inference to make predictions, and can automatically hone how they function and learn from experience, without explicit programming instructions.

Why is...