The Royal Statistical Society's Computational Statistics and Machine Learning Section are delighted to invite you to a half-day online workshop on Interpretable Machine Learning and Causal Inference, to be held on Tuesday 15th December 2020. Details and sign-up are at:
Overview:
Interpretable machine learning and causal inference are both hot topics, related in the kinds of problems they can be applied to. Each aims to address deficiencies in conventional machine learning and statistical approaches to model building. We believe researchers and practitioners working in each community have much to learn from each other, but that without first establishing common ground and defining clear boundaries, communication and collaboration will be difficult. In this workshop, we will hear from four experts about their own research investigating methods and applications in these two areas, which we hope will highlight both the commonalities and differences between them. We also anticipate a lively discussion after the presentations.
Schedule (all times GMT):
13:00 - 13:15 Introduction & welcome
13:15 - 13:55 Peter Tennant (University of Leeds & Alan Turing Institute) - "Table 2 Fallacy: Or why interpretation needs more than transparency"
13:55 - 14:00 - Break -
14:00 - 14:40 Noemi Kreif (University of York) - "Using causal machine learning to explore heterogeneous responses to policies"
14:40 - 15:05 - Break -
15:05 - 15:45 Alessandra Russo (Imperial College) - "Symbolic machine learning for interpretable AI: recent advancements and future directions"
15:45 - 15:50 - Break -
15:50 - 16:30 Vera Liao (IBM Research) - "Questioning the AI: towards human-centered interpretable machine learning"
16:30 - 17:00 Further questions/general discussion/wrap up
13:00 - 13:15 Introduction & welcome
13:15 - 13:55 Peter Tennant (University of Leeds & Alan Turing Institute) - "Table 2 Fallacy: Or why interpretation needs more than transparency"
13:55 - 14:00 - Break -
14:00 - 14:40 Noemi Kreif (University of York) - "Using causal machine learning to explore heterogeneous responses to policies"
14:40 - 15:05 - Break -
15:05 - 15:45 Alessandra Russo (Imperial College) - "Symbolic machine learning for interpretable AI: recent advancements and future directions"
15:45 - 15:50 - Break -
15:50 - 16:30 Vera Liao (IBM Research) - "Questioning the AI: towards human-centered interpretable machine learning"
16:30 - 17:00 Further questions/general discussion/wrap up
Details/sign-up: https://rss.org.uk/training-events/events/events-2020/sections/interpretable-machine-learning-causal-inferenc-(1)/
Many thanks,
Richard Tomsett
IBM United Kingdom Limited - Registered in England and Wales with number 741598.
Registered office: PO Box 41, North Harbour, Portsmouth, Hampshire PO6 3AU
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