[NEWS] The governance of artificial intelligence: interim report | UK House of Commons

On the 31st of August, the UK House of Commons published the ninth report of session 2022-23 from the Science, Innovation and Technology Committee, titled "The governance of artificial intelligence: interim report".

The Committee launched the inquiry on 20 October 2022, to examine: the impact of AI on different areas of society and the economy; whether and how AI and its different uses should be regulated; and the UK Government’s AI governance proposals. They have received and published over 100 written submissions and taken oral evidence from 24 individuals, including AI researchers, businesses, civil society representatives, and individuals affected by this technology.

This interim Report analyses the factors behind recent AI developments, highlights the benefits offered by the technology, and identifies a series of challenges for policymakers. It is examined how the UK Government has responded, and how this compares to other countries and jurisdictions.

  • Chapter 2 considers the general-purpose nature of AI.

  • Chapter 3 highlights the benefits and risks of AI for two areas of society and the economy: medicine and healthcare, and education.

  • Chapter 4 suggests challenges for policymakers that AI has created.

  • Chapter 5 examines the UK Government’s approach to AI.

  • Chapter 6 considers the international dimension of AI governance.

  • Chapter 7 outlines the next steps for the inquiry. 

The inquiry so far has led to identify twelve challenges of AI governance, that policymakers and the frameworks they design must meet.

1) The Bias challenge. AI can introduce or perpetuate biases that society finds unacceptable.

2) The Privacy challenge. AI can allow individuals to be identified and personal information about them to be used in ways beyond what the public wants.

3) The Misrepresentation challenge. AI can allow the generation of material that deliberately misrepresents someone’s behaviour, opinions or character.

4) The Access to Data challenge. The most powerful AI needs very large datasets, which are held by few organisations.

5) The Access to Compute challenge. The development of powerful AI requires significant compute power, access to which is limited to a few organisations.

6) The Black Box challenge. Some AI models and tools cannot explain why they produce a particular result, which is a challenge to transparency requirements.

7) The Open-Source challenge. Requiring code to be openly available may promote transparency and innovation; allowing it to be proprietary may concentrate market power but allow more dependable regulation of harms. 

Read it here.