Lara Macdonald

Lara is a Senior Policy Advisor in the Projects Team at the Centre for Data Ethics and Innovation (CDEI). She has worked on a policy review into bias in algorithmic decision-making across four sectors, including financial services and policing. She is now leading CDEI’s ongoing work with several organisations in the public sector to develop and test approaches to the responsible use of data and data-driven technology.

The Centre for Data Ethics and Innovation was set up by the UK Government in 2018 to drive responsible innovation by maximising the benefits of data and AI for our society and economy, that those benefits are fairly distributed across society, and creating the conditions for ethical innovation to thrive.

We do this through (1) AI monitoring, analysing and anticipating developments in the landscape, (2) working in partnership with others to tackle specific barriers to innovation, and (3) helping ministers’ to operationalise their policy interventions. In practice, this means doing detailed research and horizon-scanning work; combined with operational work to develop the understanding of public attitudes; and build the appropriate governance and technological solutions to ensure that AI is developed responsibly.

Fairness in an age of algorithms

Growth in algorithmic decision-making over the last few years has been accompanied by significant concerns about bias. In this session, Lara Macdonald, Senior Policy Advisor at the Centre for Data Ethics and Innovation (CDEI), will discuss the findings of CDEI’s recently published review into bias in algorithmic decision-making.

The review draws on a detailed analysis of the use of algorithms in four sectors (financial services, local government, policing and recruitment), proposes significant measures for government, regulators and industry to act on to tackle the risks of algorithmic bias.

The session will be particularly focused on the role that transparency can play in building a trustworthy environment and enabling responsible innovation; the need for the existing regulatory environment to evolve in order to address bias and discrimination in an algorithmic age; and the opportunity that good use of data presents to enhance fairness.