In my fellowship I have conducted groundbreaking research and spearheaded innovative initiatives, leading a team to explore and devise technological enhancements for democratic innovation. Employing a mix of artificial intelligence, design methodologies, and social science, I've amalgamated data, and analysed and compared cases to draw out lessons for democracy. I have designed software using participatory methods to craft technologies and interventions that prioritise human experience and transparency in democratic procedures. This project is driven by the aspiration of empowering citizens, bolstering the safety of communities, and heightening confidence in societal institutions through understanding which interventions build and fortify democratic activities. It involves making space for often marginalised voices, curbing hateful speech, and ensuring diverse community participation in public discourse and in governance. My focus lies in the creation of interdisciplinary data science tools that allow for analysis of political text, speech, and video. The approach involves constructing advanced models that answer pertinent research questions and navigate the ethical and social consequences of deploying algorithms in democratic engagement. The project team is committed to designing and engineering democracy software that transcend temporary successes, with an emphasis on adapting interventions to a variety of governance, workplace, and community settings. We aim to build knowledge about how techniques can be used wherever we need to make collective decisions, and deal peacefully and fairly with disagreement. We will develop AI interventions that augment existing democratic innovations, that are pragmatic and applicable in day-to-day contexts. We aspire to understand how these innovations can be sequenced optimally for transparency, trust, and mutual oversight. This involves carrying out randomised controlled trials and employing other research methods to ascertain when and how to step in to restore trust in institutions, and consequently, enhance the governance of emerging technologies. The fellowship supports me to lead a multidisciplinary research and innovation hub in the UK, one that is fervently devoted to preserving and enriching democracy. This project aims to deliver novel research involving many scientific disciplines contributing to a flourishing economy and society in the UK and beyond.

The spread of democracy has been crucial to developing a world order that has facilitated productive economic, social, and cultural growth, yet by almost any measure, democracy is in crisis. I have made a leading contribution to comparative approaches to the study of democratic innovation. I have been to the fore in drawing insights from democratic theory and empirical social science, connecting researchers with practitioners of democracy in government and society. With this fellowship, I will develop interventions to avert the crisis of democracy. Despite continued support worldwide for democracy as a regime, democracy as a practice is suffering. Issues include declining trust in government and political parties, distorted digital communications, and rising populism and polarisation in politics. In a positive response, governments, businesses, and charities are already reimagining democracy. They have designed inventive democratic services and devices that can help sustain democratic order. Some examples include participatory budgeting, randomly selected juries, and different forms of referendums. These social innovations are often supported by civic technologies, open data applications, citizen science, and behavioural nudges such as information cues that increase civic volunteering. Yet we know little about what works beyond case studies. I have taken a leading role in the development of a comparative research agenda. A number of projects such as participedia.net (of which I am a co-investigator and executive member), have begun to collect systematic data on how these devices improve democracy (or do not). Despite the abundance of information, research has yet to take advantage of the analytic potential of data science and new technologies. In the project, I will bring together traditional survey data, and new forms of crowdsourced and real-time data to understand what interventions actually help to sustain rather than hinder democracy. In the first step, the project will push the frontiers of knowledge about what has worked before and what has not. I will use set-theoretic methods, at the cutting edge of comparative analysis in the social sciences to determine the conditions in the past that have been necessary and sufficient for increases in positive democratic behaviours. The method can establish which combinations of conditions in different contexts achieved democratic improvements such as inclusion, learning, deliberation, and support for institutions. At the University of Southampton, I am uniquely positioned among prominent social and computer scientists to lead a multi-disciplinary research team in developing indicators and data analytics for democratic innovation. I will harness available data to provide the necessary information on developing political contexts to guide policymakers in the development and choice of instruments for democratic decision-making. My work will reduce wasted resources in public consultation. These indicators will include new measures of the extent to which debates are consolidating in the public sphere using social media data, argument mapping and opinion polling. Economic indicators of government capacity, as well as indicators of civil society capacity and the levels demand for inputs from citizens will be incorporated to complement those data. The ultimate aim of the project is to use advances in traditional and new forms of data analysis, to work in accordance with the best that democratic theory and political philosophy has to offer. The project will involve agile design of indicator dashboards and complementary social interventions. In conjunction with international and national experts in public engagement, we will deliver field experiments to test feasibility of designs. The fellowship will allow me to lead a multi-disciplinary research agenda developing data science that responds to and integrates the lessons of democratic theory and empirical social science.

SOUNDSCALE is an ambitious research project aimed at transforming urban planning in smart cities through the innovative use of Distributed Acoustic Sensing (DAS) technologies, leveraing legacy optical fibre cables that lie unused underground or undersea. DAS is currently used to sense vibration/sounds in its surroundings to detect events like earthquakes or monitor oil rigs. Recently, it has been proposed as a cheap and effective alternative to other monitoring systems in urban environments, such as to monitor traffic, crowds, buildings' integrity, and transportation networks in general, which could influence how cities are planned in the future. However, there are important concerns around how this technology develops such as data privacy, AI ethics, equitable technology access, sustainability, climate impact, inequality, and citizen participation in decision-making processes. In an age where technological advancements rapidly alter the urban landscape, there is a growing disconnect between citizens, policymakers and these transformative changes. The vision of SOUNDSCALE is to enable cities to become truly 'smart' by integrating citizens directly into the development and implementation of emerging technologies, so that they can prioritise and anticipate issues before it is too late to change the direction of research and development. This approach not only aims to mitigate potential ethical, privacy, and accessibility issues but also to ensure that technology deployment is sustainable, inclusive, and beneficial to all segments of society. To realise this vision, SOUNDSCALE adopts an interdisciplinary research strategy, integrating insights from the physical sciences, political science, human geography, humanities, environmental sciences, arts-based research, computer science and public health, intertwined with an ambitious knowledge exchange and engagement strategy. The project will be divided into three phases. In Phase 1, a diverse citizen panel from London and Southampton will be convened to identify research priorities based on learning about the technology's opportunities and risks. These cities have an extensive optical fibre network connected through the National Dark Fibre Facility (NDFF), which will be used to obtain preliminary measurements of 'the sound of the cites'. Phase 2 involves interdisciplinary workshops to translate these priorities into actionable research areas, fostering innovative methodologies and novel interdisciplinary knowledge. Phase 3 focuses on synthesising findings for impact with policymakers and the public, ensuring that the research benefits are tangible and aligned with societal needs. SOUNDSCALE emphasises the importance of co-creation with non-academics, including practitioners, activists, artists, policymakers, and citizens. This collaborative philosophy is designed to produce research that is not only academically rigorous but also socially relevant and responsive to the needs and concerns of the wider community. Through this process, SOUNDSCALE seeks to create interdisciplinary research projects involving researchers from different disciplines to tackle problems like: disentangling urban background noise from dynamic events, exploring the link between noise exposure and public health across sections of society, developing ethical frameworks for DAS deployment or examining how DAS can redefine urban spaces and influence social inequalities and surveillance. Our knowledge exchange and engagement strategy is innovative, featuring a citizen panel, policy activities, artistic exhibitions, an art- and activism-led grant program, and a sustained digital and local presence. By partnering with a wide range of stakeholders, including universities, research centres, art galleries, industry partners, and city councils, SOUNDSCALE aims to ensure that its findings and technologies are widely disseminated and adopted, leading to more inclusive, equitable, and smart urban development.

The physiological and cognitive impacts of extreme temperatures are known in general and mostly for healthy populations, but little is known about how impacts differ across the diversity of the UK population; in particular, those with multiple health conditions, including neurological, who are likely to be the most impacted, and for which interventions (e.g. green spaces) are poorly targeted or non-existent. Furthermore, we do not have tools to integrate available data to understand temperature-health risks nationally and at the necessary individual and household level, and therefore how to target interventions. More broadly, the evidence is lacking to guide policy on the coupled challenges of health inequalities, urban planning and climate change mitigation/adaptation, under uncertain futures of climate and demographic change. Increases in heatwaves are a robust aspect of climate change, with associated increases in health-related deaths. Cold-related mortality has declined with overall warming, yet still far outweighs the increase in heat-related deaths, and the overall burden of cold-related illness and mortality will remain high with an ageing population. Most research on health outcomes has focused on excess mortality rates and limited to broad vulnerability groupings. Health outcomes are, however, much more nuanced, being related to both physical and mental health and exacerbated by underlying conditions including neurological and mental ill health, with exposure related to context specific temperature-humidity thresholds. Therefore, there are significant gaps in our understanding of health risks (including long-term outcomes) for the most vulnerable, and how this relates to the interplay between variability of temperature hazards and outdoor/indoor exposure as driven by socio-economic gradients and mobility. We therefore envisage developing new knowledge and tools for precise risk assessment and targeted interventions, focused on disproportionately impacted groups. By doing so, we will transform our understanding of the drivers of inequalities in temperature related health outcomes and propose using this to inform policy on levelling up and pathways to climate targets. We will realise our vision through an ambitious but feasible, highly multidisciplinary project that is necessary to address this complex problem. Our aim is to transform our understanding of the risk of temperature impacts on health outcomes for vulnerable populations of England and Wales with particular focus on health inequalities, currently and for future scenarios, and identify environmental solutions, directly addressing the overarching funding call objective. Our approach is multi-scale, with high granularity in both space and time: a) linking national scale risk assessment with detailed urban case studies to understand risks at the level of streets/buildings and vulnerable groups; and b) identifying how risks change with future changes in climate, demographics, mobility and health status. National scale assessment will reveal how extremes evolve across climatic gradients and land types, and we will explore the diversity of health outcomes and identify landscape configurations and socio-economic factors that are likely to lead to higher risks, and therefore potential mitigations that are resilient to future change. Community engagement will tease out the nuances of impacts and acceptability of environmental and community-based interventions. This will feed back to the national scale to inform on mitigation, via risk reduction tools for early warning, planning and policy. Our approach will provide a far more nuanced, informed and precise risk assessment than currently exists that will allow targeted interventions to be identified, providing risk reduction where most needed.

It is now widely accepted that if current private car use trends continue then urban road networks will become increasingly unable to cope with the demand for travel, with existing traffic management techniques unable to achieve desired levels of both sustainability and safety. While much research effort has been directed towards this issue there has been a dichotomy between supply side solutions (for example flow responsive traffic signals) and demand side solutions (such as encouraging high occupancy vehicles and public transport use). The ultimate merging of these two approaches would result in signal priority being given based on an environmentally friendly vehicle occupancy scale (from hybrid/electric public transport at one end to single occupancy large engine cars at the other) with clear sustainability, economic and environmental benefits. The required real-time data sources and technologies to achieve this are only now beginning to be created however and forward looking research is now essential to shape the characteristics of these data sources and quantify the benefits which they facilitate. Since the introduction of demand responsive traffic signal control in the 1970s, urban traffic control (UTC) systems have attempted to optimise traffic signal stage lengths and stage orders based on real time traffic detector data. While much research has been carried out since this time to improve the optimality of the underlying algorithms however, the initial data source of inductive loop or above ground (e.g. infrared) detectors have remained fundamental to the operation of the system. In order to give the maximum opportunity for a set of traffic signals to react to approaching traffic, the detectors used to provide the input data for each arm of the junction are generally located as far upstream as possible often the exit stream from the upstream junctions. While this reliance on upstream detectors gives the greatest warning of approaching traffic it also means that the UTC system must make estimations of the stop line arrival times of vehicles, suffering from errors related to platoon dispersion and indeed the variable speed nature of urban driving. The development of GPS/Galileo technologies for individual vehicle positioning, accompanied by advances in wireless communications technologies however provides increasing opportunity to establish the position of vehicles not just at a single upstream detector location, but continuously along the approaching arm. This would provide the UTC systems with significant increased detail in relation to real-time traffic demand, allowing for more detailed stage adjustments and a transformation from the current discrete decision approach to one of continuous response to approaching demands.The focus of this research is therefore the creation of traffic signal control algorithms based on the real-time positions of individual vehicles and, through the creation of a simulation test bed, the quantification of the benefits in relation to the reductions (compared to existing signal control methods) in both delays and emissions that such an algorithm could achieve, a critical step towards achieving an environmentally and economically sustainable road transport system.