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Towards compliant and accountable systems

Department of Computer Science and Technology

Data-driven technology increasingly underpins everyday life. But what happens when it fails? Who is, or should be, responsible, given the complexity of these systems and their supply-chains? How do we hold those responsible to account when things do go wrong? What is needed to govern the development and use of technologies so that they better accord with social values?

These socio-technical questions are particularly pertinent as systems become more pervasive and complex; technical environments increasingly data driven, autonomous and physical; and as the grand visions of smart cities, the Internet of Things, and of course, “AI” become a reality.

In line with this, technology and its impact on society are the subject of much public discussion and regulatory attention – the EU’s General Data Protection Regulation is a prominent example. There is growing demand for better accountability regarding the technology that influences everyday life, not least given the scandals now being reported almost daily.

Addressing issues of accountability and legal compliance of new and emerging technologies can help ensure that those technologies are built and deployed in alignment with social norms, that they remain appropriate and fit for purpose, and that those responsible can be held to account as and when necessary.

Towards this, the Compliant and Accountable Systems research group considers how to better align technology with legal and policy concerns, and vice-versa. The team, based at the Department of Computer Science & Technology (Computer Laboratory), is multi-disciplinary, with its members having backgrounds in computer science, law, and policy. Our research involves analyses and interventions—both technical and legal—in areas around governance, agency, empowerment, compliance, and accountability as they relate to new and emerging technologies. Some current research themes include: engineering for rights, centralisation vs decentralised data/compute, the reviewability of the design and use of machine learning, issues of online content distribution, and meaningful audit and interrogation of complex systems, to name a representative few.

Find out more about the group and its research:


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