Explainable Machine Learning in Deployment Post COVID-19
As countries around the world begin to slowly emerge from the global pandemic, many hope that the intense disruption facing organisations will diminish. Yet for Machine Learning practitioners, this ‘next normal’ is rife with challenges. The last few months have brought enormous disruption to how people, organisations and technology behave. How can Machine Learning stay resilient and effective when model assumptions are based on behaviours that are no longer prevalent? This talk will explore actions that data practitioners can take to mitigate the impact of COVID-19 on models in development and deployment. It will offer an in-depth perspective on Explainable AI (XAI) techniques as a foundation for more resilient Machine Learning.
Torgyn is a Senior Consultant in Data Science at QuantumBlack, where she is also co-leading R&D on Explainable AI. Torgyn’s area of expertise is algorithmic transparency, sparse data modelling, survival analyses, and data-efficient modelling; she authored multiple peer-reviewed publications on those subjects since 2014. Torgyn has over 7 years of applied Machine Learning experience both in industry and academia. She was an Honorary Researcher at Nuffield Department of Primary Care Health Science of the University of Oxford and is a founder of Next Generation Programmers outreach initiative for rural developing countries. Torgyn holds a PhD in Engineering and BEng in Computer and Information Engineering, both from the University of Warwick, UK.