The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.
On September 15, Russ Greiner —a PhD Candidate at the University of Alberta — presented “Learning Models that Predict Objective, Actionable Labels” at the AI Seminar.
Abstract:
Many medical researchers want a tool that “does what a top medical clinician does, but does it better”. This presentation explores this goal. This requires first defining what “better” means, leading to the idea of outcomes that are “objective” and then to ones that are actionable, with a meaningful evaluation measure. We will discuss some of the subtle issues in this exploration – what does “objective” mean, the role of the (perhaps personalized) evaluation function, multi-step actions, counterfactual issues, distributional evaluations, etc. Collectively, this analysis argues we should learn models whose outcome labels are objective and actionable, as that will lead to tools that are useful and cost-effective.
Watch the full presentation below:
Image credits: Amii Intelligence
By: Scott Lilwall
originally published at: amii
Source: cyberpogo.com
