WHY TAKE A UNIDIMENSIONAL APPROACH TO TOOLS THAT EXIST IN HIGH DIMENSIONAL SPACE? скачать в хорошем качестве

WHY TAKE A UNIDIMENSIONAL APPROACH TO TOOLS THAT EXIST IN HIGH DIMENSIONAL SPACE?

Allison Cohen is the Applied AI Projects Lead at Mila. In this role, Allison works closely with AI researchers, collaborators and funding partners to professionalize socially beneficial AI projects and deploy them at scale. Currently, her portfolio of projects includes a de-biasing application and a tool to support police in their human trafficking investigations. Allison is also involved with the Global Partnership on Artificial Intelligence (GPAI), an international organization tasked with developing global best practices on AI. Within GPAI, Allison works with the Drug Discovery Committee on a series of policy recommendations designed to catalyze an AI-enabled R&D process that produces drugs that are equitably distributed and better aligned with healthcare outcomes. Allison holds a BA in International Development from McGill University and an MA in Global Affairs from the University of Toronto. Conference : Why take a unidimensional approach to tools that exist in high dimensional space? Friday 6 may 2022, 11h30 - 12h15 — Amphi orange There is a problematic and yet ubiquitous phenomena in the field of AI research and product development: we’re too late to ask ourselves whether the algorithms we’ve been developing are built for purpose. Why? Because we’ve been misinterpreting “fit for purpose” to mean technically feasible. This definition obscures considerations of equal importance including the cultural, social, political and legal landscape that permeate the tool’s design and determine the tool’s utility. When building AI products, researchers are only confronted with questions of multidisciplinary reflection as they’re about to submit a paper or launch their tool. However, at this point in the project, a host of relevant decisions have already been made, whether consciously or not, that influence the algorithms’ utility. These decisions begin before the algorithm has ever been trained or the data has ever been collected. In this talk, I will discuss important points of inflection that are too often missed in the product development lifecycle. These inflection points present opportunities for AI researchers and product developers to ensure that the technology they’re building is fit for purpose