Building Fair AI: Practical Guide to Ethical Algorithms
Learn practical principles for designing fair AI algorithms. Master ethical considerations in healthcare, criminal justice, and machine learning.
Learn practical principles for designing fair AI algorithms. Master ethical considerations in healthcare, criminal justice, and machine learning.
This comprehensive course teaches ten practical principles for designing fair algorithms in high-stakes applications. Students explore real-world case studies in healthcare, criminal justice, and large language models like ChatGPT. The curriculum emphasizes understanding bias by age, gender, nationality, race, and other attributes, while providing concrete strategies for algorithmic fairness. Designed for a broad audience from high school students to professionals, the course requires no coding experience and focuses on practical implementation of fair AI principles.
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What you'll learn
Understand widely used definitions of fairness and bias
Master principles for training fair predictive models
Learn to design ethical healthcare algorithms
Develop skills in documenting algorithm intended uses
Gain practical experience in assessing algorithmic bias
Master techniques for transparent algorithm design
Skills you'll gain
This course includes:
199 Minutes PreRecorded video
17 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides a comprehensive framework for designing and implementing fair AI algorithms. Through four detailed modules, students learn fundamental concepts of algorithmic fairness, practical design principles, and documentation requirements. The curriculum covers essential topics including bias detection, model transparency, and ethical considerations in various applications. Special emphasis is placed on real-world case studies in healthcare and criminal justice, along with practical implementation strategies for ensuring algorithmic fairness.
Introduction
Module 1 · 1 Hours to complete
Designing Algorithms
Module 2 · 1 Hours to complete
Documenting Algorithms
Module 3 · 51 Minutes to complete
Algorithms in the hands of humans
Module 4 · 1 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
AI Innovator Advancing Health Equity Through Data Science
Emma Pierson serves as a distinguished instructor at Fred Hutchinson Cancer Center and is a computer scientist at Cornell University, where she specializes in using AI and data science to address health disparities across demographic groups. Her academic credentials include a bachelor's in physics and master's in computer science from Stanford, a master's in statistics from Oxford as a Rhodes Scholar, and a doctorate in computer science from Stanford. As the instructor for "Practical Steps for Building Fair AI Algorithms," she brings her expertise in developing ethical AI solutions that address systemic inequalities in healthcare and social systems. Her groundbreaking research includes analyzing COVID-19 transmission patterns, racial disparities in traffic stops, and women's health data across 109 countries. Beyond academia, she contributes to major publications like The New York Times and The Atlantic, making complex data science concepts accessible to broader audiences. Her work has directly influenced policy changes, including the Los Angeles Police Department's revision of their stop practices and state health departments' COVID-19 reopening strategies
Champion of Ethical AI and Algorithmic Fairness
Kowe Kadoma serves as an instructor at Fred Hutchinson Cancer Center, specializing in ethical AI implementation and algorithmic fairness. As an instructor for "Practical Steps for Building Fair AI Algorithms," she focuses on teaching professionals how to develop and implement AI systems that promote equity and fairness across diverse populations. Her course curriculum emphasizes practical approaches to understanding and addressing bias in AI algorithms, covering crucial topics such as ethical data collection, model training principles, and healthcare algorithm design. Through her teaching, she helps students master the complexities of algorithmic fairness definitions, transparency in AI systems, and the challenging ethical dilemmas that arise in AI implementation. Her approach combines theoretical understanding with hands-on application, ensuring students gain practical skills in designing and implementing fair AI solutions while considering their broader societal impact.
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