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

Practical Steps for Building Fair AI Algorithms

Taught in English

Some content may not be translated

Course

Gain insight into a topic and learn the fundamentals

Emma Pierson
Kowe Kadoma

Instructors: Emma Pierson

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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Recently updated!

November 2023

Assessments

13 quizzes, 4 assignments

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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 readings3 quizzes1 assignment

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 readings4 quizzes1 assignment

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 readings2 quizzes1 assignment

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

What's included

6 videos3 readings4 quizzes1 assignment

Instructors

Emma Pierson
Fred Hutchinson Cancer Center
1 Course276 learners

Offered by

Recommended if you're interested in Algorithms

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