This is the third in a series of three blogs on AI assurance, which explore the key concepts and practical challenges for developing an AI assurance ecosystem. The first blog focused on current confusion around AI assurance tools and the …
This is the second in a series of three blogs on AI assurance, which explore the key concepts and practical challenges for developing an AI assurance ecosystem. The first blog focused on the need for AI assurance and the CDEI’s …
Data-driven technologies, such as artificial intelligence (AI), have the potential to bring about significant benefits for our economy and society. However, they also introduce risks that need to be managed. As these technologies are more widely adopted, there is an …
In our recently published review into bias in algorithmic decision-making, we explored the regulatory context in which algorithmic decisions take place, which includes equality law, human rights law, discrimination law and sector specific regulations. The main piece of legislation that …
The Race Disparity Unit (RDU) and Centre for Data Ethics and Innovation (CDEI) began a partnership in March 2019 at the start of the CDEI’s review into bias in algorithmic decision-making. The RDU is a UK government unit which collates, …
The CDEI believes that the government should introduce a mandatory transparency obligation on all public sector organisations using algorithms that have a significant influence on significant decisions affecting individuals. Our report published last week suggests definitions for these terms. But whilst a transparent approach is vital to building a trustworthy environment, we should not assume that greater transparency from public sector organisations will inevitably lead to greater trust in the public sector.
This report draws together the findings and recommendations from a broad range of work. We have focused on the use of algorithms in significant decisions about individuals, looking across four sectors (recruitment, financial services, policing and local government), and making cross-cutting recommendations that aim to help build the right systems so that algorithms improve, rather than worsen, decision-making.
Financial companies are increasingly using complex algorithms to make decisions regarding loans or insurance - algorithms that look for patterns in data which are associated with risks of default or high insurance claims. This raises risks of bias and discrimination …