Actionable Strategies for AI Impact Assessments

This module equips you with actionable strategies to conduct AI impact assessments, ensuring ethical, legal, and social considerations are integrated into every stage of your AI projects.
Format

Online
Course

Expert

Leah
Davis

Live session date

January 21, 2026

Live session time

12:30 - 3:00PM

Individual preparatory work

1 Hour

Price

$250 + Tax

About the module

In today's fast-paced economy, the ability to develop and deliver innovative services and products swiftly is a critical competitive advantage. However, the rapid integration of AI technology can introduce unforeseen risks and negative impacts, which may be harmful or even illegal. This module on Responsible AI Impact Assessments will provide an in-depth overview of algorithmic harms and guide you through each stage of an AI impact assessment framework equipping you with the knowledge and tools to conscientiously and responsibly plan and manage AI projects. Additionally, in this module you will outline how impact assessments compare to other frameworks and examine their role in establishing appropriate AI governance mechanisms and complying with upcoming regulations.

Learning Outcomes

Identify how impact assessments are an important tool in establishing AI governance mechanisms and meeting upcoming regulations. 
Describe different types of harms from algorithmic systems, specifically AI systems.
Outline different stages of an impact assessment process.
Highlight the key activities that need to take place in different stages of an impact assessment.
Discuss how impact assessments can be considered throughout an AI system/product’s  life cycle.
Consciously plan how an impact assessment could be considered in implementation of a responsible AI strategy in various AI projects. 

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 participants with an interdisciplinary background or interest, who have a foundational understanding of AI systems, including their creation and diverse applications.

Course Lessons

Leah Davis

Leah Davis is a PhD student in the Responsible Autonomy Intelligent Systems Ethics (RAISE) Lab in the Department of Electrical and Computer Engineering at McGill University, with affiliations at Mila – Quebec AI Institute, Université du Québec à Montréal, and Université de Montréal. She is a 2025 Pierre Trudeau Scholar and McGill Engineering Vadasz Doctoral Fellowship Scholar. Her research centres upon socially responsible AI evaluations, particularly within algorithmic auditing practices. She approaches this work through a regulatory lens, aiming to highlight the critical role of system integration within the AI policy ecosystem. Her perspective is particularly informed by industry experience in roles within requirements engineering, software quality assurance, and medical device regulation. She holds an MSc in Social Data Science from the University of Oxford and a BEng in Biomedical Engineering Co-op from the University of Guelph.