Focus areas
Research Themes
- 01
Algorithmic 'reviewability' and 'contestability'
Contrasting with the general focus on 'explanation', we consider that which is necessary to facilitate the review, understanding, scrutiny and challenge of algorithmic and automated decision-making (ML) systems.
- 02
Meaningful transparency
Exploring requirements and mechanisms for facilitating the meaningful inspection and interrogation of socio-technical systems and their behaviour(s). One focus is on auditable augmented/mixed/virtual reality systems, as well as the Internet of Things, and the use of machine learning in various contexts.
- 03
Algorithmic supply chains
Investigating how responsibility, control and opacity issues occur across the components of modern AI systems — which consists of multiple actors and multiple components.
- 04
Data protection and privacy enhancing technologies (PETs)
Considering issues, critiques, management and interventions regarding personal and confidential data.
- 05
Algorithmic bias / fairness
Focusing on trade-offs in various context, and methods and tooling for supporting practitioners.
- 06
Platforms and online harms
Exploring the design, use and abuse of platforms in perpetuating harms. The current focus is on social media (recommender systems), virtual user-spaces (games/XR), and cloud services, including 'AI as a Service'.
- 07
Decision provenance
Considering how tracing the flow of data can be leveraged to assist accountability in complex, automated and ML-driven environments.
- 08
Compliance and rights engineering
How systems can be better built to be (demonstrably) compliant with legal obligations, and to account for rights – fundamental, group and individual.
- 09
Centralised v. decentralised infrastructures
Considering the potential of data management and compute infrastructures, and their legal, regulatory and policy implications. Currently looking at personal data stores and data trusts.