A leading rheumatologist teamed up with an AI specialist to help with a systematic review of lupus research, a condition of which 90% of patients are women. Together, they revealed the deep-rooted biases in research methodologies - and the machine learning models that underpin them.

Professor Coziana Ciurtin (UCL) and Mirela Gyurova (Kubrick) share the story behind their GenAI tool which can revolutionise underfunded and underexplored areas of medical research, including identifying ML-enforced biases.

When gender and ethnic disparities in healthcare persist, what responsibilities do data professionals have in shaping ethical, impactful AI? And how can partnerships between industry and academia unlock new standards for evidence, equity, and trust in ML?

For anyone building, using, or regulating AI, this session will challenge assumptions and make the case for responsible, cross-sector innovation.

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Scientific discovery hinges on the effective integration of metadata, which refers to a set of 'cognitive' operations such as determining what information is relevant for inquiry, and data, which encompasses physical operations such as observation and experimentation. This talk introduces the Causal Modelling Agent (CMA), a novel framework that synergizes the metadata-based reasoning capabilities of Large Language Models (LLMs) with the data-driven modelling of Deep Structural Causal Models (DSCMs) for the task of causal discovery. We evaluate the CMA's performance on a number of benchmarks, as well as on the real-world task of modelling the clinical and radiological phenotype of Alzheimer's Disease (AD). Our experimental results indicate that the CMA can outperform previous data-driven or metadata-driven approaches to causal discovery. In our real-world application, we use the CMA to derive new insights into the causal relationships among biomarkers of AD.