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Improving responsible access to demographic data to address bias

Following our review into bias in algorithmic decision-making, the CDEI has been exploring challenges around access to demographic data for detecting and mitigating bias in AI systems, and considering potential solutions to address these challenges.  Today we are publishing our …

Fairness Innovation Challenge: Call for Use Cases

Building and using AI systems fairly can be challenging, but is hugely important if the potential benefits from better use of AI are to be achieved.  Recognising this, the government's recent white paper “A pro-innovation approach to AI regulation” proposes …

Types of assurance in AI and the role of standards

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 …

The need for effective AI assurance

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 …

Public Sector Equality Duty and bias in algorithms

Posted by: , Posted on: - Categories: Algorithms, Bias, Facial recognition technology

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 …

An overview of the CDEI's review into bias in algorithmic decision-making

Posted by: , Posted on: - Categories: Algorithms, Bias, Decision-making

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.