AI holds great promise in addressing several grand societal challenges, including the development of a smarter, cleaner electricity grid, the seamless provision of convenient on-demand mobility services, and the ability to protect citizens through advice and informed deployment of medical, emergency and police resources to fight epidemics, deal with crises and prevent crime. However, these promises can only be realised if citizens trust AI systems.

In this fellowship, I will develop the fundamental science needed to build trusted citizen-centric AI systems. These AI systems will put citizens at their heart, rather than view them as passive providers of data. They will make decisions that maximise the benefit for citizens, given their individual constraints and preferences. They will use incentives where appropriate to encourage positive behaviour change, but they will also be robust to strategic manipulation, in order to prevent individuals from exploiting the system at the expense of others. Importantly, citizen-centric AI systems will involve citizens and other stakeholders in a feedback loop that enables them to audit decisions and modify the system's behaviour to ensure that effective but also ethical decisions are taken.

Achieving this vision of citizen-centric AI systems requires several novel advances in the area of artificial intelligence.

First, to safeguard the privacy of individuals, new approaches to understanding the constraints and preferences of citizens are needed. These approaches will be distributed in nature - that is, they will not depend on collecting detailed data from individuals, but will allow citizens to manage and retain their own data. To achieve this, I will develop intelligent software agents that act on behalf of each citizen, that store personal data locally and only communicate limited information to others when necessary.

Second, to incentivise positive behaviour modifications and to discourage exploitation, I will draw on the field of mechanism design to model how self-interested decision-makers behave in strategic settings and how their actions can be modified through appropriate incentives. A particular challenge will be to deal with limited information, uncertainty about preferences and a constantly changing environment that necessitates incentives to be dynamically adapted via appropriate learning mechanisms.

Finally, to enable an inclusive feedback loop involving citizens and other stakeholders, new interaction mechanisms are needed that can provide explanations for actions as well as information about whether the system is making fair decisions. While there is a wealth of emerging work on explainability and fairness in AI, this typically deals with simple one-shot problems. In contrast, I will consider more realistic and complex sequential settings, where actions have long-term consequences (including on fairness) that may not be immediately apparent.

As part of the fellowship, I will work with a range of partners to put the research into practice and generate real impact.

With EA Technology and the Energy Systems Catapult, I will work on incentive-aware smart charging mechanisms for electric vehicles. With Dstl and UTU Technologies, I will develop disaster response applications that use crowdsourced intelligence from citizens to provide situational awareness, track the spread of infectious diseases or issue guidance to citizens. With Siemens, Jaguar Land Rover, Thales and the Connected Places Catapult, I will develop new approaches for trusted on-demand mobility. With Fawley Waterside, I will work on citizen-centric solutions to smart energy and transportation in the Southampton area. With Dstl and Thales, I will explore further applications to national security and policing. Finally, with IBM Research, I will develop new explainability and fairness tools, and integrate these with their existing open source frameworks (AI Fairness 360 and AI Explainability 360).