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Ian Moura
and
Shannon Frattaroli, PhD, MPH
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?
Read more
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?
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/iqbxvw/exploring
What's inside
Syllabus
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.
Read more
Syllabus
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.
Read more
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces students to the fundamental concept of algorithms and algorithmic bias in the context of public policy, providing a solid foundation for understanding these complex issues
Facilitates discussion and critical engagement on algorithmic bias among policymakers and agency leaders, fostering a deeper understanding of its implications for fairness, justice, and equity
Empowers learners to play an active role in reducing algorithmic bias, equipping them with practical strategies and knowledge to make informed decisions and advocate for equitable policies
Lays the groundwork for understanding the connections between algorithmic bias and broader societal power structures, highlighting the need for systemic change
Requires learners to have prior knowledge of policy-related areas to fully grasp the course content, which may limit accessibility for those new to the field
Does not provide a certificate of completion, which may affect learners' motivation and external recognition of their participation
Save this course
Save Exploring Algorithmic Bias as a Policy Issue: A Teach-Out to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Exploring Algorithmic Bias as a Policy Issue: A Teach-Out with these
activities:
Explore cases where algorithmic bias has been identified
Show steps
Examining real-world examples of algorithmic bias can help you better understand its various forms and prepare you for the discussions in the course.
Browse courses on
Algorithmic Bias
Show steps
-
Identify news articles or research papers that discuss specific instances of algorithmic bias.
-
Summarize the key takeaways from each case study, including the type of algorithm, the data used, and the resulting bias.
Practice using fairness evaluation tools
Show steps
Getting hands-on experience with fairness evaluation tools will equip you to identify and address potential biases in algorithms during the course.
Browse courses on
Algorithmic Fairness
Show steps
-
Find online tutorials or documentation on fairness evaluation tools such as Fairness 360 or Aequitas.
-
Follow the tutorials to install and use the tools.
-
Apply the tools to evaluate a sample dataset and identify any potential biases.
Engage in discussions on algorithmic bias on online forums
Show steps
Participating in online discussions can expose you to diverse perspectives and insights on algorithmic bias, enriching your understanding of the topic.
Show steps
-
Join online forums or discussion groups related to algorithmic bias.
-
Actively participate in discussions, sharing your thoughts and engaging with others.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve practice problems on algorithmic fairness metrics
Show steps
Practicing with algorithmic fairness metrics will enhance your ability to assess and mitigate bias in algorithms, a key skill for the course.
Show steps
-
Find online resources or textbooks that provide practice problems on algorithmic fairness metrics.
-
Solve the problems and compare your solutions with provided answers or discuss them with peers.
Develop a presentation on algorithmic bias for a specific audience
Show steps
Creating a presentation requires you to synthesize your understanding of algorithmic bias and communicate it effectively to others, reinforcing your learning.
Show steps
-
Identify a specific audience for your presentation, such as policymakers, industry professionals, or community groups.
-
Research and gather information on algorithmic bias tailored to your audience's interests and knowledge level.
-
Develop a clear and engaging presentation structure, including an introduction, key points, and a conclusion.
Develop a plan for addressing algorithmic bias in a specific policy or organizational context
Show steps
This project will allow you to apply the knowledge gained in the course to a real-world scenario, deepening your understanding of the challenges and strategies for addressing algorithmic bias.
Show steps
-
Identify a specific policy or organizational context where algorithmic bias is a concern.
-
Research and analyze the potential sources and impacts of algorithmic bias in that context.
-
Develop a plan outlining specific actions and measures that can be taken to address and mitigate the identified biases.
Explore cases where algorithmic bias has been identified
Show steps
Examining real-world examples of algorithmic bias can help you better understand its various forms and prepare you for the discussions in the course.
Browse courses on
Algorithmic Bias
Show steps
Show steps
Show steps
- Identify news articles or research papers that discuss specific instances of algorithmic bias.
- Summarize the key takeaways from each case study, including the type of algorithm, the data used, and the resulting bias.
Practice using fairness evaluation tools
Show steps
Getting hands-on experience with fairness evaluation tools will equip you to identify and address potential biases in algorithms during the course.
Browse courses on
Algorithmic Fairness
Show steps
Show steps
Show steps
- Find online tutorials or documentation on fairness evaluation tools such as Fairness 360 or Aequitas.
- Follow the tutorials to install and use the tools.
- Apply the tools to evaluate a sample dataset and identify any potential biases.
Engage in discussions on algorithmic bias on online forums
Show steps
Participating in online discussions can expose you to diverse perspectives and insights on algorithmic bias, enriching your understanding of the topic.
Show steps
Show steps
Show steps
- Join online forums or discussion groups related to algorithmic bias.
- Actively participate in discussions, sharing your thoughts and engaging with others.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve practice problems on algorithmic fairness metrics
Show steps
Practicing with algorithmic fairness metrics will enhance your ability to assess and mitigate bias in algorithms, a key skill for the course.
Show steps
Show steps
Show steps
- Find online resources or textbooks that provide practice problems on algorithmic fairness metrics.
- Solve the problems and compare your solutions with provided answers or discuss them with peers.
Develop a presentation on algorithmic bias for a specific audience
Show steps
Creating a presentation requires you to synthesize your understanding of algorithmic bias and communicate it effectively to others, reinforcing your learning.
Show steps
Show steps
Show steps
- Identify a specific audience for your presentation, such as policymakers, industry professionals, or community groups.
- Research and gather information on algorithmic bias tailored to your audience's interests and knowledge level.
- Develop a clear and engaging presentation structure, including an introduction, key points, and a conclusion.
Develop a plan for addressing algorithmic bias in a specific policy or organizational context
Show steps
This project will allow you to apply the knowledge gained in the course to a real-world scenario, deepening your understanding of the challenges and strategies for addressing algorithmic bias.
Show steps
Show steps
Show steps
- Identify a specific policy or organizational context where algorithmic bias is a concern.
- Research and analyze the potential sources and impacts of algorithmic bias in that context.
- Develop a plan outlining specific actions and measures that can be taken to address and mitigate the identified biases.
Career center
Learners who complete Exploring Algorithmic Bias as a Policy Issue: A Teach-Out will develop knowledge and skills
that may be useful to these careers:
Policy Analyst
Policy Analyst
Policy Analysts research, analyze, and make recommendations regarding policy. A good Policy Analyst is well-versed in policy issues, and has the tools to investigate and evaluate existing policies. By taking Exploring Algorithmic Bias as a Policy Issue: A Teach-Out, you will learn to identify algorithmic bias and understand its implications for existing policy, making you the ideal hire for positions in policy research and evaluation.
Data Scientist
Data Scientist
Data Scientists use mathematical and statistical tools to find trends and patterns in data. They also develop algorithms to solve problems and automate tasks. Understanding algorithmic bias will help you create and use algorithms effectively and responsibly.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. A background in algorithmic bias will help you build software that is fair and equitable.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. Understanding algorithmic bias will help you create and use machine learning models effectively and responsibly.
Product Manager
Product Manager
Product Managers oversee the development and launch of new products. A background in algorithmic bias will help you create products that are fair and equitable.
Policy Advisor
Policy Advisor
Policy Advisors advise policymakers on the development and implementation of public policy. An understanding of algorithmic bias can help you to inform policymakers about the potential risks and benefits of using algorithms in policymaking.
Public Policy Analyst
Public Policy Analyst
Public Policy Analysts research, analyze, and make recommendations on public policy issues. A background in algorithmic bias will help you understand the potential impact of algorithms on public policy.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. An understanding of algorithmic bias will help you to identify and correct for bias in data.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data. An understanding of algorithmic bias will help you to identify and correct for bias in data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and statistical tools to solve problems and improve efficiency. A background in algorithmic bias will help you to identify and solve problems caused by algorithmic bias.
Urban Planner
Urban Planner
Urban Planners develop plans for the development and use of land. An understanding of algorithmic bias will help you to create plans that are fair and equitable.
Transportation Engineer
Transportation Engineer
Transportation Engineers design and maintain transportation systems. A background in algorithmic bias will help you to create transportation systems that are fair and equitable.
Environmental Engineer
Environmental Engineer
Environmental Engineers design and implement solutions to environmental problems. A background in algorithmic bias will help you to create solutions that are fair and equitable.
Civil Engineer
Civil Engineer
Civil Engineers design and build infrastructure projects. A background in algorithmic bias will help you to create infrastructure projects that are fair and equitable.
Architect
Architect
Architects design and build buildings. A background in algorithmic bias will help you to create buildings that are fair and equitable.
For more career information including salaries, visit:
OpenCourser.com/course/iqbxvw/exploring
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Exploring Algorithmic Bias as a Policy Issue: A Teach-Out.
Save
Book provides a critical examination of the rise of algorithms and their impact on society, including their potential for bias and discrimination.
Book explores the dangers of algorithmic bias and the need for algorithmic accountability and transparency.
Book provides real-world examples of the harmful effects of algorithmic bias on marginalized communities.
Provides a practical and technical approach to designing and evaluating fair and ethical algorithms.
Provides practical guidance on designing and implementing data science projects, including best practices for mitigating bias.
Explores the ways in which search engines perpetuate and reinforce racial bias.
Provides a technical introduction to differential privacy, a mathematical framework for protecting individual privacy in the context of data analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/iqbxvw/exploring
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Via
Coursera
Institution
Johns Hopkins University
Instructors
Ian Moura
Shannon Frattaroli, PhD, MPH
Language
English
Good to know
Introduces students to the fundamental concept of algorithms and algorithmic bias in the context of public policy, providing a solid foundation for understanding these complex issues
Facilitates discussion and critical engagement on algorithmic bias among policymakers and agency leaders, fostering a deeper understanding of its implications for fairness, justice, and equity
Empowers learners to play an active role in reducing algorithmic bias, equipping them with practical strategies and knowledge to make informed decisions and advocate for equitable policies
Lays the groundwork for understanding the connections between algorithmic bias and broader societal power structures, highlighting the need for systemic change
Requires learners to have prior knowledge of policy-related areas to fully grasp the course content, which may limit accessibility for those new to the field
Does not provide a certificate of completion, which may affect learners' motivation and external recognition of their participation
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