Exploring Algorithmic Bias as a Policy Issue: A Teach-Out

This Teach Out does not issue certificates of completion. Algorithms – and algorithmic bias – are making regular appearances in the news, and increasingly, are being recognized as a policy issue. But what is an algorithm, exactly? And what does it mean when someone describes an algorithm as biased? This Teach-Out will encourage policy makers, agency leaders, and others in similar positions to identify algorithms that are already in use and make connections to broader ideas about fairness, justice, and equity. After completing the Teach-Out, learners will be able to participate in discussions around algorithmic bias, inform others about how algorithms can perpetuate existing disparities, and take steps to reduce the impact of algorithmic bias on the people and communities they serve.

• Welcome to the Course
• Welcome to the course & Orientation
• What is an Algorithm?
• This module provides a definition of what algorithms are and how they are used, particularly within the context of specific policies and policy-related areas. It also invites learners to think about the ways algorithms are being integrated into their own area of focus.
• What Does It Mean for an Algorithm To Be Biased?
• This module explains what it means for an algorithm to be biased and discusses potential sources of bias within an algorithm. Learners will also have the opportunity to think through the ways that specific choices about outcomes and measurement often facilitate algorithmic bias.
• Algorithmic Bias and Systemic Bias
• This module explores the connections between algorithmic bias and other forms of systemic discrimination. Learners will also explore the ways that choices about using algorithms often reflect societal power and inequality.
• Anticipating and Addressing Algorithmic Bias
• This final module will highlight specific steps that can help reduce the risk and impact of algorithmic bias on people and communities. Learners will also identify others with whom they can share what they have learned about the ways algorithms may perpetuate and heighten existing disparities.