Unpacking Bias: Ensuring Algorithmic Fairness in AI Models

Techniques for incorporating fairness into AI models and minimizing bias.

Format

Online
Course

Expert

Maryam Molamohammadi

Live session date

May 14 , 2025

Live session time

12:30 - 3:00PM

Individual preparatory work

1 Hour

Price

$225 + Tax

About the module

In today's digital age, algorithms wield significant influence across various domains, yet they often harbor biases that can perpetuate inequalities. Understanding and unpacking these biases in algorithms is crucial for ensuring equitable outcomes in automated processes. In this course you will unpack the different sources of bias in algorithms and discover technical bias mitigation strategies to create more equitable and trustworthy AI systems.

Learning Outcomes

Unpack the various sources of bias in algorithms to understand their origins and impacts 
Assess the implications of biased algorithms on real-world applications.
Identify best practices for implementing fairness into AI systems and explore technical tools.
Articulate the unique challenges of fairness in Generative AI.

Who is this module for?


CTO, machine learning engineers, data scientists, AI researchers, and AI product developers, Marketing/communications professionals.

Tailored for participants with a foundational understanding of AI concepts and basic knowledge of probability, linear algebra and machine learning.

Course Lessons

Maryam Molamohammadi

Maryam Molamohammadi is a responsible AI advisor and researcher. She leads the product-focused responsible AI advisory at Mila, where they provide comprehensive analysis to ensure AI products are developed responsibly. This process involves harm mapping and then risk mitigation planning tailored to ML-based products. Her research interests include algorithmic harm mitigation techniques (more specifically algorithmic fairness) as well as democratic elements in AI governance.
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