Fred Hutchinson Cancer Center
Practical Steps for Building Fair AI Algorithms
Fred Hutchinson Cancer Center

Practical Steps for Building Fair AI Algorithms

Emma Pierson
Kowe Kadoma

Instructors: Emma Pierson

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

5 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

5 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Understand widely used definitions of fairness and bias

  • Master principles to follow when training models

  • Design a healthcare algorithm

  • Reason about challenging algorithmic fairness dilemmas

Details to know

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Assessments

17 assignments

Taught in English

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There are 4 modules in this course

In this module, you'll learn the basic concepts this course relies on: what an algorithm is, and why fairness is tricky and subtle to define. We'll start by defining what a predictive algorithm even is, because this course is designed to be accessible to students who have never taken a computer science class. (If you have taken a previous class on predictive algorithms or machine learning, feel free to skip this section.) Then we'll jump right into fairness. This course will present ten practical fairness lessons, and in this module we'll discuss two of them. We'll also give a sneak preview of how the lessons of this course apply to generative AI models like ChatGPT.

What's included

12 videos2 readings4 assignments

This module will cover fundamental lessons for designing fair algorithms: what data they should be trained on, what features they should use to predict, and what outcomes they should predict.

What's included

6 videos4 readings5 assignments

This module discusses the importance of documenting algorithms and datasets so they are used only in settings where they are appropriate.

What's included

5 videos2 readings3 assignments

This module discusses the complex interplay between algorithmic predictions and human decisions.

What's included

6 videos3 readings5 assignments

Instructors

Emma Pierson
Fred Hutchinson Cancer Center
1 Course428 learners

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Recommended if you're interested in Algorithms

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