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.

We are applying for Theme 3, focussing on the skills, career development and training needs of data professionals who support data delivery within Trusted Research Environments (TREs). Our network of regional TREs in Scotland serve as vital hubs for health and social care data research using deidentified, unconsented data to improve public health outcomes. The Safe Haven Network functions under the Scottish Safe Haven Charter since 2014 and has dedicated data professionals who identify, curate, link and deliver deidentified data to researchers. This includes critical information governance work supporting the 'Five Safes' framework for secure access and release of outputs through disclosure checking. Currently, opportunities to enhance projects with a social science lens are being missed, and social science researchers may be frustrated in their attempts to integrate important datasets with health and social care data. By investing in the development of TRE staff in utilising social science data, we aim to address challenges and bridge existing skill gaps. We want health-focussed TRE networks to feel more open to social science researchers, to maximise innovative research in the public good. Our project will design and implement a tailored training program to equip data professional staff with the necessary skills and knowledge to effectively harness the potential of social science data. Improved accessibility and diversity of data research in TREs will foster more inclusive and interdisciplinary research. This work could support the roll out of training for the related new network of research Secure Data Environments (SDEs) in England. Our project will be delivered in 4 work packages: 1. Identifying training needs: We will conduct stakeholder engagement, including data service professional staff, social science data controllers and researchers, to gain a thorough understanding of their challenges and training requirements. Through surveys and workshops, we will develop a gap and needs report to inform workforce development. Additionally, we will review job descriptions, onboarding or induction materials, and past projects to identify areas for improvement. 2. Curriculum development: Building on this gap and needs analysis, we will develop a comprehensive training curriculum. We will use our established learning design workshop methodology to collaboratively produce a tiered curriculum in partnership with experts. This will provide foundation level training introducing fundamental concepts for all, building to intermediate and advanced levels for individuals with deeper interest in supporting social science research. 3. Training content creation and evaluation: we will create engaging and informative training materials mapped to the curriculum. Leveraging our advanced online education and learning design expertise, we will develop online modules, interactive exercises, and integrated case studies using social science datasets produced in partnership with our TRE network. Throughout the development process, we will evaluate the effectiveness of content through learning analytics and iteratively improve content with continuous user feedback and testing across our TRE network. 4. Future strategy and sustainability: As we near the completion of the project, we will submit the training content to be approved as credit bearing modules aligned with the Scottish Credit and Qualifications Framework (SCQF) and explore options to ensure training content is accredited or recognised by relevant Professional, Statutory and Regulatory Bodies (PSRBs). We will engage further with key stakeholders to garner support for a newly designed career pathway to allow selected TRE data professionals to develop social science expertise, mapped to our curriculum and training material.