Responsible Integration of Generative AI

Generative AI offers new capabilities that can enhance business processes, but its integration requires understanding risks and carefully considering ethical, technical, and social implications for responsible implementation.
Format

Online
Course

Expert

Simon Blackburn

Live session date

February 25, 2025

Live session time

12:30 - 3:00

Individual preparatory work

1 Hour

Price

$250 + Tax

About the module

This module focuses on enabling professionals to responsibly integrate generative AI tools into systems such as customer support chatbots, content generation workflows, or triage applications. Participants will explore the unique challenges of generative AI, will learn to evaluate generative AI tools, anticipate and mitigate risks like hallucination or misuse, and implement effective monitoring processes. Through interactive group work, participants will apply their knowledge to practical scenarios, ensuring their organization can harness the potential of generative AI tools while maintaining ethical and operational standards.

Learning Outcomes

Understand the unique risks of generative AI: Recognize how generative AI differs from other AI systems.
Evaluate Generative AI Tools for Organizational Fit: Assess prebuilt generative AI tools to determine their appropriateness for specific business applications.
Implement Technical Guardrails: Learn about techniques such as red-teaming, adversarial testing, and jailbreak prevention to maintain system reliability.
Establish frameworks for review, build-in of guardrails, empowerment of technical and non-technical teams.

Who is this module for?

All AI professionals, including executive leaders, Data Scientists, ML/AI engineers, AI developers, AI product managers, AI consultants and investors.

Tailored for all participants but is recommended for those with a foundational understanding of AI concepts and basic technical knowledge of AI systems.


Course Lessons

Simon Blackburn

Simon Blackburn holds a Ph.D. in Physics from the Université de Montréal. After a brief period as a consultant in the industry, he completed a postdoctoral fellowship under the supervision of Yoshua Bengio at Mila from 2017 to 2018. His research focuses on the applications of Graph Neural Network (GNN) models for the prediction of chemical properties of molecules. Since 2018, he has held the role of Senior Scientist in the Applied Research team at Mila, where he has worked with several companies in different fields with a particular interest in the scientific applications of machine learning.
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