Algorithmic Bias: Policy Implications and Solutions
Explore algorithmic bias, its societal impact, and develop strategies to address this critical policy issue.
Explore algorithmic bias, its societal impact, and develop strategies to address this critical policy issue.
This Teach-Out course examines algorithmic bias as a pressing policy concern, providing policymakers and leaders with essential knowledge to navigate this complex issue. The course defines algorithms and algorithmic bias, explores their widespread use in various sectors, and investigates the sources and impacts of bias. Participants will learn to identify algorithms in use, understand how bias manifests, and recognize the connections between algorithmic bias and systemic discrimination. The course emphasizes practical approaches to mitigate bias, encouraging learners to develop strategies for addressing this issue in their professional contexts. By the end, participants will be equipped to engage in informed discussions about algorithmic bias, raise awareness of its implications, and take concrete steps to reduce its impact on communities they serve.
Instructors:
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What you'll learn
Understand what algorithms are and how they are used in various policy areas
Identify sources and manifestations of algorithmic bias
Recognize connections between algorithmic bias and systemic discrimination
Develop strategies to address and mitigate algorithmic bias
Learn to communicate effectively about algorithmic bias issues
Understand the policy implications of algorithmic decision-making
Skills you'll gain
This course includes:
3.5 Hours PreRecorded video
1 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 5 modules in this course
This Teach-Out course explores algorithmic bias as a critical policy issue, designed for policymakers, agency leaders, and professionals in related fields. The curriculum is divided into five modules, covering the fundamentals of algorithms, the nature and sources of algorithmic bias, its relationship to systemic bias, and strategies for addressing this issue. Participants learn to identify algorithms in use, understand how bias manifests in algorithmic systems, and recognize the broader societal implications of algorithmic decision-making. The course emphasizes practical approaches to mitigating bias, including participatory methods in algorithm development, addressing bias at various stages of the algorithmic lifecycle, and strategies for raising awareness and initiating change within organizations. Throughout the course, learners engage with real-world examples, expert insights, and interactive activities to deepen their understanding and develop actionable strategies for their professional contexts.
Welcome to the Course
Module 1 · 6 Minutes to complete
What is an Algorithm?
Module 2 · 2 Hours to complete
What Does It Mean for an Algorithm To Be Biased?
Module 3 · 2 Hours to complete
Algorithmic Bias and Systemic Bias
Module 4 · 1 Hours to complete
Anticipating and Addressing Algorithmic Bias
Module 5 · 2 Hours to complete
Fee Structure
Instructors
Health Policy Research Scholar at Johns Hopkins University Addressing Algorithmic Bias in Healthcare
Ian Moura is a Health Policy Research Scholar at Johns Hopkins University, focusing on the intersection of algorithmic decision-making and health policy, particularly as it impacts disabled individuals. His course, Exploring Algorithmic Bias as a Policy Issue: A Teach-Out, addresses how systemic biases in algorithms can exacerbate health disparities and aims to empower disabled people by recognizing their experiences and identities in healthcare discussions.
Associate Professor at Johns Hopkins University Specializing in Injury Prevention and Public Health Policy
Shannon Frattaroli, PhD, MPH, is an Associate Professor of Health Policy and Management at the Johns Hopkins Bloomberg School of Public Health. She serves as the Deputy Director and Associate Director for Outreach at the Center for Injury Research and Policy. Dr. Frattaroli teaches courses on Public Health Policy Formulation, Qualitative Research Methods, and Implementation Research and Practice. Her research is centered on developing policy strategies to prevent injuries from various sources, including motor vehicle crashes, opioid misuse, and gun violence, particularly focusing on firearm-related domestic violence. She is dedicated to translating research findings into effective policies that enhance public health outcomes.
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Frequently asked questions
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