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Algorithmic Decision Making - Exploring Practical Approaches to Liability, Fairness, and Explainability

Offered By: Toronto Machine Learning Series (TMLS) via YouTube

Tags

Algorithmic Decision-Making Courses Data Science Courses Machine Learning Courses Fairness Courses Responsible AI Courses Ethics in AI Courses

Course Description

Overview

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Explore practical approaches to liability, fairness, and explainability in algorithmic decision-making through this 53-minute conference talk from the Toronto Machine Learning Series (TMLS). Gain insights from a panel of experts including Patrick Hall, Principal Scientist at bnh.ai, Talieh Tabatabaei, Data Scientist at TD Bank, and Richard Zuroff, Advisor at Element AI. Delve into the critical aspects of responsible AI implementation, understanding the challenges and solutions in creating transparent, fair, and accountable algorithmic systems. Learn how to navigate the complex landscape of AI ethics and governance in real-world applications across various industries.

Syllabus

Algorithmic Decision Making: Exploring Practical Approaches to Liability, Fairness, & Explainability


Taught by

Toronto Machine Learning Series (TMLS)

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