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Grassroots Responsible AI - Operationalizing AI Ethics

Toronto Machine Learning Series (TMLS) via YouTube

Overview

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Attend a comprehensive workshop on operationalizing responsible AI and machine learning ethics. Learn from industry experts as they present practical techniques for implementing ethical AI practices using machine learning observability. Discover how to use statistical distance checks to monitor features and model output in production, analyze the effects of changes on models, and employ explainability techniques to determine if issues are model or data related. Gain insights into achieving model transparency as a crucial first step in managing responsible AI. Participate in a panel discussion featuring thought leaders in responsible, fair, and ethical AI who will share diverse perspectives and actionable insights on approaching responsible AI from the ground up. Benefit from the expertise of speakers including Aparna Dhinakaran (Arize AI), Amber Roberts (Arize AI), Abigail Hing Wen (A Media, Inc.), and Reid Blackman (Virtue Consultants) as they explore the intersection of AI ethics, machine learning observability, and practical implementation strategies.

Syllabus

Grassroots Responsible AI Operationalizing AI Ethics From

Taught by

Toronto Machine Learning Series (TMLS)

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