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Virtual event Data for the public good

Accountable AI: a route to effective regulation

Exploring the foundational premises for delivering ‘world-leading data protection standards’ that benefit people and achieve societal goals

Uses of data: research, justice and medical research
Date and time
2:00pm – 3:00pm, 20 October 2021 (BST)

On 10 September, the UK Government published its proposal for amending the current data protection regime (the UK GDPR). The aim is to create ‘a pro-growth and pro-innovation data regime whilst maintaining the UK’s world-leading data protection standards’.

At the Ada Lovelace Institute, our mission is to ensure that data and AI work for people and society. In order to explore whether the Government’s plans will enable these aims, we are organising a series of five events, each looking at different sections, questions, statements and framing in the Government’s consultation and asking what benefits and challenges are brought by the proposals.

Session 3: Accountable AI: a route to effective regulation

Wednesday 20 October, 14:00-15:00 BST

Watch the event back here:

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Chair

Panellists

  • David Erdos

    Co-Director CIPIL, University of Cambridge
  • Lillian Edwards

    Professor of Law, Innovation & Society, Newcastle University
  • Cosmina Dorobantu

    Director for Public Policy, Alan Turing Institute
  • Jennifer Cobbe

    Senior Research Associate, Department of Computer Science and Technology, University of Cambridge
  • Divij Joshi

    PhD candidate, UCL Faculty of Laws

The third event in our series focuses on accountable AI. This theme is prevalent through chapters 1 and 2 of the consultation, particularly the sections on automated decision-making and accountability frameworks, where the Government suggests accountability mechanisms present barriers to innovation.

This event will interrogate the assumption that accountability measures are barriers and whether they can also deliver benefits to society, and explore questions including:

  • What does a robust accountability framework for data and AI systems look like?
  • How do we ensure accountability systems go beyond tick-box exercises and make a positive difference?
  • How can we design appropriate obligations, rights and controls to match the ethical, legal and societal implications of automated decision making?

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