Over the last decade, the street has emerged as one of the primary sites where everyday publics encounter AI. Industry and public sector organisations have deployed a variety of AI-based technologies in UK streets, from autonomous vehicles (AVs) to navigation apps, data-driven modelling in smart city projects and facial recognition technologies (FRT). These deployments have been accompanied by significant policy initiatives defining societal benefits of AI-driven innovation (safety, levelling up, sustainability, inclusion) as well as institutional engagements with affected communities through policy exhibitions, user-centred workshops and citizen cafés. However, from the perspective of the street, AI innovation often manifests as a messy social reality, provoking frictions that exceed existing frameworks for responsible innovation: in Cambridge, firefighters battling a fire had to move a delivery robot that was in their way, while in Australia suburbs were left without electricity after a food delivery drone made an emergency landing on top of a set of powerlines. There remain, then, significant divergences between the general frameworks for responsible AI and the particular lived realities of AI in the street. To build capacity among everyday publics and AI innovation consortia to engage across such divides, this 6-month project will develop a situated, creative approach to public engagement with AI: street-level observatories of everyday AI. To bridge divides between lay and expert understandings of AI innovation, we will evaluate and prototype a set of street-level observatories for everyday AI. The aim of these observatories is to explore how everyday publics perceive and engage with AI at a primary site - city streets - where specific transformations, benefits, harms and (ir)responsibilities of AI in society can be made visible and thus legible for both publics and stakeholders. To realise this, we will collaborate with local partners and the arts to trial creative interventions that invite people on the street to observe the effects of AI in the lived environment. Our scoping project will 1) build partnerships across the humanities, arts and social sciences and with organisations and groups committed to situated forms of public engagement with AI-based science and innovation in connected and automated cities. In partnership with local government, we will 2) trial street-level AI observatories in 4 diverse UK cities—Cambridge, Coventry, London and Edinburgh—and one international location, Logan (Australia). The observatories will combine digital, place-based and/or embodied approaches, such as data walks and sensor media (apps) and will be designed to support shared learning across the project teams and partners. Trialling AI observatories in city streets will enable us to undertake 3) a joint process of evaluating and prototyping an everyday AI observatory. This will make visible the entanglement of everyday social life with AI, showing people and technologies in complex real-world settings where sectoral, disciplinary and specialist interests intersect. This will be a space of interest to partners in local and national government, public policy innovation, and AI scientists and industry representatives, and create opportunities for developing shared understandings of societal responses and priorities between industry, policymakers, researchers and everyday publics.

The current AI paradigm at best reveals correlations between model input and output variables. This falls short of addressing health and healthcare challenges where knowing the causal relationship between interventions and outcomes is necessary and desirable. In addition, biases and vulnerability in AI systems arise, as models may pick up unwanted, spurious correlations from historic data, resulting in the widening of already existing health inequalities. Causal AI is the key to unlock robust, responsible and trustworthy AI and transform challenging tasks such as early prediction, diagnosis and prevention of disease. The Causality in Healthcare AI with Real Data (CHAI) Hub will bring together academia, industry, healthcare, and policy stakeholders to co-create the next-generation of world-leading artificial intelligence solutions that can predict outcomes of interventions and help choose personalised treatments, thus transforming health and healthcare. The CHAI Hub will develop novel methods to identify and account for causal relationships in complex data. The Hub will be built by the community for the community, amassing experts and stakeholders from across the UK to 1) push the boundaries of AI innovation; 2) develop cutting-edge solutions that drive desperately needed efficiency in resource-constrained healthcare systems; and 3) cement the UK's standing as a next-gen AI superpower. The data complexity in heterogeneous and distributed environments such as healthcare exacerbates the risks of bias and vulnerability and introduces additional challenges that must be addressed. Modern clinical investigations need to mix structured and unstructured data sources (e.g. patient health records, and medical imaging exams) which current AI cannot integrate effectively. These gaps in current AI technology must be addressed in order to develop algorithms that can help to better understand disease mechanisms, predict outcomes and estimate the effects of treatments. This is important if we want to ensure the safe and responsible use of AI in personalised decision making. Causal AI has the potential to unearth novel insights from observational data, formalise treatment effects, assess outcome likelihood, and estimate 'what-if' scenarios. Incorporating causal principles is critical for delivering on the National AI Strategy to ensure that AI is technically and clinically safe, transparent, fair and explainable. The CHAI Hub will be formed by a founding consortium of powerhouses in AI, healthcare, and data science throughout the UK in a hub-spoke model with geographic reach and diversity. The hub will be based in Edinburgh's Bayes Centre (leveraging world-class expertise in AI, data-driven innovation in health applications, a robust health data ecosystem, entrepreneurship, and translation). Regional spokes will be in Manchester (expertise in both methods and translation of AI through the Institute for Data Science and AI, and Pankhurst Institute), London (hosted at KCL, representing also UCL and Imperial, leveraging London's rapidly growing AI ecosystem) and Exeter (leveraging strengths in philosophy of causal inference and ethics of AI). The hub will develop a UK-wide multidisciplinary network for causal AI. Through extended collaborations with industry, policymakers and other stakeholders, we will expand the hub to deliver next-gen causal AI where it is needed most. We will work together to co-create, moving beyond co-ideation and co-design, to co-implementation, and co-evaluation where appropriate to ensure fit-for-purpose solutions Our programme will be flexible, will embed trusted, responsible innovation and environmental sustainability considerations, will ensure that equality diversity and inclusion principles are reflected through all activities, and will ensure that knowledge generated through CHAI will continue to have real-world impact beyond the initial 60 months.