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

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.

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