Title: ML for Molecules and Materials 

Abstract: While Generative AI is entertaining the general public with artificially generated videos of puppies playing in the snow, a silent disruption is taking place behind the scenes where the same technology is deployed to generate molecules for new drugs and materials. While admittedly less entertaining, the (positive) impact this can have for society might be even bigger than ChatGPT and its descendants.  Society is facing very serious challenges concerning climate change, sustainability, the energy transition, resistant bacteria strains, pandemics and so on. If we could accelerate the process of designing and manufacturing new drugs and materials this could help society avert some potential future disasters. In this talk I will discuss how modern AI can help the sciences, and reversely, how deep insights from physics and mathematics can improve modern AI. 

Title: Interactive Explanations for Contestabile AI 

Abstract: AI has become pervasive in recent years, but state-of-the-art approaches mostly neglect the need for AI systems to be contestable. Contestability is advocated by AI guidelines (e.g. by the OECD) and regulation of automated decision-making (e.g. GDPR). In contrast,  there has been little attention in AI to suggest how contestability requirements can be met computationally. Contestability requires dynamic (human-machine or machine-machine) decision-making processes, whereas much of the current AI landscape is tailored to static AIs – thus the need to accommodate contestability will require a radical rethinking. In this talk I will argue that  computational forms of contestable AI will require forms of explainability whereby machines and humans can interact,  and that computational argumentation can support the needed interactive explainability for contestability. 

Title: Architecting normative infrastructure for pluralistic AI value alignment 

Abstract: Aligning AI systems with human values is crucial for their safe and beneficial deployment. However, “human values” have remained an elusive and multifaceted target. In this talk, I introduce a framework for bringing rigor to conceptualizing, communicating, and systematizing the normative process of value specification for AI alignment. 

Title: The Future of Healthcare: Unveiling the Potential of AI-enabled In-silico Trials in Medical Innovation 

Abstract: The rapid introduction of novel medical technologies necessitates swift, reliable scientific validation of their safety and efficacy to protect patient welfare. Traditional clinical trials, while essential, face challenges such as detecting low-frequency side effects, high costs, and practical limitations, especially with paediatric patients, rare diseases, and underrepresented ethnic groups. In-silico trials (IST), powered by Computational Medicine, offer a promising solution by using computer simulations to test medical products on virtual patient populations. This approach allows for the a-priori optimisation of clinical outcomes, thorough risk assessment, and failure mode analysis before human trials. Although in-silico evidence is still emerging, it has the potential to revolutionise health and life sciences R&D and regulatory processes. The UK’s leadership in IST could significantly enhance its global standing in health and life sciences, boost the economy, and ensure early access to innovative health products for its citizens.