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Emma Pierson
and
Kowe Kadoma
Algorithms increasingly help make high-stakes decisions in healthcare, criminal justice, hiring, and other important areas. This makes it essential that these algorithms be fair, but recent years have shown the many ways algorithms can have biases by age, gender, nationality, race, and other attributes. This course will teach you ten practical principles for designing fair algorithms. It will emphasize real-world relevance via concrete takeaways from case studies of modern algorithms, including those in criminal justice, healthcare, and large language models like ChatGPT. You will come away with an understanding of the basic rules to follow when trying to design fair algorithms, and assess algorithms for fairness.
Read more
Algorithms increasingly help make high-stakes decisions in healthcare, criminal justice, hiring, and other important areas. This makes it essential that these algorithms be fair, but recent years have shown the many ways algorithms can have biases by age, gender, nationality, race, and other attributes. This course will teach you ten practical principles for designing fair algorithms. It will emphasize real-world relevance via concrete takeaways from case studies of modern algorithms, including those in criminal justice, healthcare, and large language models like ChatGPT. You will come away with an understanding of the basic rules to follow when trying to design fair algorithms, and assess algorithms for fairness.
Read more
Algorithms increasingly help make high-stakes decisions in healthcare, criminal justice, hiring, and other important areas. This makes it essential that these algorithms be fair, but recent years have shown the many ways algorithms can have biases by age, gender, nationality, race, and other attributes. This course will teach you ten practical principles for designing fair algorithms. It will emphasize real-world relevance via concrete takeaways from case studies of modern algorithms, including those in criminal justice, healthcare, and large language models like ChatGPT. You will come away with an understanding of the basic rules to follow when trying to design fair algorithms, and assess algorithms for fairness.
This course is aimed at a broad audience of students in high school or above who are interested in computer science and algorithm design. It will not require you to write code, and relevant computer science concepts will be explained at the beginning of the course. The course is designed to be useful to engineers and data scientists interested in building fair algorithms; policy-makers and managers interested in assessing algorithms for fairness; and all citizens of a society increasingly shaped by algorithmic decision-making.
Register for this course and see more details by visiting:
OpenCourser.com/course/k30yc2/practical
What's inside
Syllabus
Introduction
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.
Read more
Syllabus
Introduction
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.
Read more
Designing Algorithms
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.
Documenting Algorithms
This module discusses the importance of documenting algorithms and datasets so they are used only in settings where they are appropriate.
Algorithms in the hands of humans
This module discusses the complex interplay between algorithmic predictions and human decisions.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores issues of algorithmic fairness, which are particularly relevant in today's data-driven society
Presents ten practical principles for designing fair algorithms, making the course highly applicable
Emphasizes real-world relevance through case studies of modern algorithms, including those used in criminal justice, healthcare, and large language models like ChatGPT
Taught by Emma Pierson and Kowe Kadoma, both experienced professionals in the field of algorithmic fairness
Suitable for a broad audience, including students in high school or above with an interest in computer science and algorithm design, as well as engineers, data scientists, policy-makers, and managers
Does not require prior coding experience, making it accessible to beginners
Save this course
Save Practical Steps for Building Fair AI Algorithms 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 Practical Steps for Building Fair AI Algorithms with these
activities:
Review principles of fairness
Show steps
Refresh your knowledge of fairness principles to build a stronger foundation for this course.
Browse courses on
Algorithmic Fairness
Show steps
-
Read the course syllabus and course description to understand the course objectives and key topics.
-
Review notes or textbooks from previous courses on ethics in computer science or algorithmic fairness.
-
Complete online tutorials or watch videos that provide an overview of fairness principles in machine learning and artificial intelligence.
Organize Your Learning Materials
Show steps
Ensure a smooth learning experience by organizing your notes, assignments, and resources.
Show steps
-
Create a dedicated folder or notebook for this course.
-
Regularly review and organize your notes to improve retention.
Attend the 'Data Science for Social Good' Meetup
Show steps
Connect with professionals in the field and engage in discussions about the ethical and fair use of algorithms.
Browse courses on
Data Science
Show steps
-
Find and attend a local meetup or online event focused on data science for social good.
-
Introduce yourself to others and share your interests in fair algorithm design.
-
Participate in discussions and learn from the experiences of others.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Solve Fairness-Related Algorithm Problems on LeetCode
Show steps
Sharpen your algorithm design skills with targeted practice on fairness-related problems.
Browse courses on
Algorithms
Show steps
-
Identify and attempt to solve fairness-related algorithm problems on LeetCode.
-
Analyze the solutions and discuss them with peers or mentors to identify areas for improvement.
Analyze real-world examples of algorithmic bias
Show steps
Develop practical skills in identifying and analyzing algorithmic bias through real-world examples.
Show steps
-
Gather news articles, research papers, or case studies that highlight examples of algorithmic bias in various domains.
-
Analyze the examples to identify the type of bias, its potential impact, and the underlying factors contributing to it.
-
Discuss the ethical implications and potential solutions to address the identified biases.
Review "Artificial Intelligence: A Textbook for Ethical Deployment"
Show steps
Prepare yourself for ethical considerations when designing algorithms and deploying AI models.
Browse courses on
Ethical AI
Show steps
-
Read each chapter thoroughly and make notes on key concepts, such as fairness, bias, and algorithmic transparency.
-
Participate in online discussion forums about the book's content and engage with other learners.
Learn techniques for designing fair algorithms
Show steps
Acquire knowledge and skills in applying techniques to design algorithms that are fair and unbiased.
Show steps
-
Explore online tutorials, workshops, or courses that teach best practices for designing fair algorithms.
-
Follow along with guided examples that demonstrate how to implement fairness-aware machine learning techniques.
-
Experiment with different bias mitigation techniques and evaluate their effectiveness in addressing specific types of bias.
Participate in a 'Bias in AI' Workshop
Show steps
Gain practical insights and techniques for mitigating bias in algorithms through interactive workshops.
Browse courses on
Bias in AI
Show steps
-
Find and register for a workshop on bias in AI.
-
Actively participate in discussions and exercises.
-
Apply the knowledge gained to your own projects and assignments.
Try Coursera's 'Algorithmic Decision Making' Specialization
Show steps
Gain a comprehensive understanding of the challenges and techniques involved in designing fair and responsible algorithms.
Show steps
-
Enroll in the Coursera specialization and complete the interactive modules and assignments.
-
Apply the principles learned to real-world scenarios and use the provided case studies to practice.
Develop a presentation on a fairness case study
Show steps
Enhance your understanding of algorithmic fairness by researching and presenting a real-world case study.
Browse courses on
Case Study Analysis
Show steps
-
Choose a specific case study of algorithmic bias or fairness that interests you.
-
Research the case study in-depth, gathering information from news articles, research papers, and other relevant sources.
-
Develop a presentation that clearly outlines the context, bias identified, ethical implications, and potential solutions.
-
Practice delivering your presentation to effectively communicate your findings and insights.
Design and Implement an Algorithm for Fairness
Show steps
Demonstrate your understanding of fair algorithm design by creating and implementing an algorithm that addresses real-world bias.
Browse courses on
Algorithms
Show steps
-
Identify a specific domain or problem where bias in algorithms is known to exist.
-
Research and analyze existing techniques for addressing bias in algorithms.
-
Design and implement your own algorithm that incorporates fairness principles.
-
Evaluate the performance of your algorithm using appropriate metrics and compare it to existing algorithms.
-
Document your findings and share your algorithm with the community.
Attend a workshop on algorithmic fairness
Show steps
Stay up-to-date with the latest research and best practices in algorithmic fairness by attending relevant workshops.
Browse courses on
Fairness in Machine Learning
Show steps
-
Research and identify workshops or conferences that focus on algorithmic fairness and ethical AI.
-
Attend the workshop and actively participate in discussions, presentations, and hands-on activities.
-
Network with experts and fellow attendees to gain insights and expand your knowledge.
Contribute to Fairness-Focused Open-Source Projects
Show steps
Engage with the open-source community and contribute to projects that promote fair and equitable algorithms.
Browse courses on
Open Source
Show steps
-
Identify open-source projects focused on fairness in algorithms.
-
Contribute to the documentation, code, or issue tracking of these projects.
-
Collaborate with other contributors to improve the quality and impact of the projects.
Review principles of fairness
Show steps
Refresh your knowledge of fairness principles to build a stronger foundation for this course.
Browse courses on
Algorithmic Fairness
Show steps
Show steps
Show steps
- Read the course syllabus and course description to understand the course objectives and key topics.
- Review notes or textbooks from previous courses on ethics in computer science or algorithmic fairness.
- Complete online tutorials or watch videos that provide an overview of fairness principles in machine learning and artificial intelligence.
Organize Your Learning Materials
Show steps
Ensure a smooth learning experience by organizing your notes, assignments, and resources.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for this course.
- Regularly review and organize your notes to improve retention.
Attend the 'Data Science for Social Good' Meetup
Show steps
Connect with professionals in the field and engage in discussions about the ethical and fair use of algorithms.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find and attend a local meetup or online event focused on data science for social good.
- Introduce yourself to others and share your interests in fair algorithm design.
- Participate in discussions and learn from the experiences of others.
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Solve Fairness-Related Algorithm Problems on LeetCode
Show steps
Sharpen your algorithm design skills with targeted practice on fairness-related problems.
Browse courses on
Algorithms
Show steps
Show steps
Show steps
- Identify and attempt to solve fairness-related algorithm problems on LeetCode.
- Analyze the solutions and discuss them with peers or mentors to identify areas for improvement.
Analyze real-world examples of algorithmic bias
Show steps
Develop practical skills in identifying and analyzing algorithmic bias through real-world examples.
Show steps
Show steps
Show steps
- Gather news articles, research papers, or case studies that highlight examples of algorithmic bias in various domains.
- Analyze the examples to identify the type of bias, its potential impact, and the underlying factors contributing to it.
- Discuss the ethical implications and potential solutions to address the identified biases.
Review "Artificial Intelligence: A Textbook for Ethical Deployment"
Show steps
Prepare yourself for ethical considerations when designing algorithms and deploying AI models.
Browse courses on
Ethical AI
Show steps
Show steps
Show steps
- Read each chapter thoroughly and make notes on key concepts, such as fairness, bias, and algorithmic transparency.
- Participate in online discussion forums about the book's content and engage with other learners.
Learn techniques for designing fair algorithms
Show steps
Acquire knowledge and skills in applying techniques to design algorithms that are fair and unbiased.
Show steps
Show steps
Show steps
- Explore online tutorials, workshops, or courses that teach best practices for designing fair algorithms.
- Follow along with guided examples that demonstrate how to implement fairness-aware machine learning techniques.
- Experiment with different bias mitigation techniques and evaluate their effectiveness in addressing specific types of bias.
Participate in a 'Bias in AI' Workshop
Show steps
Gain practical insights and techniques for mitigating bias in algorithms through interactive workshops.
Browse courses on
Bias in AI
Show steps
Show steps
Show steps
- Find and register for a workshop on bias in AI.
- Actively participate in discussions and exercises.
- Apply the knowledge gained to your own projects and assignments.
Try Coursera's 'Algorithmic Decision Making' Specialization
Show steps
Gain a comprehensive understanding of the challenges and techniques involved in designing fair and responsible algorithms.
Show steps
Show steps
Show steps
- Enroll in the Coursera specialization and complete the interactive modules and assignments.
- Apply the principles learned to real-world scenarios and use the provided case studies to practice.
Develop a presentation on a fairness case study
Show steps
Enhance your understanding of algorithmic fairness by researching and presenting a real-world case study.
Browse courses on
Case Study Analysis
Show steps
Show steps
Show steps
- Choose a specific case study of algorithmic bias or fairness that interests you.
- Research the case study in-depth, gathering information from news articles, research papers, and other relevant sources.
- Develop a presentation that clearly outlines the context, bias identified, ethical implications, and potential solutions.
- Practice delivering your presentation to effectively communicate your findings and insights.
Design and Implement an Algorithm for Fairness
Show steps
Demonstrate your understanding of fair algorithm design by creating and implementing an algorithm that addresses real-world bias.
Browse courses on
Algorithms
Show steps
Show steps
Show steps
- Identify a specific domain or problem where bias in algorithms is known to exist.
- Research and analyze existing techniques for addressing bias in algorithms.
- Design and implement your own algorithm that incorporates fairness principles.
- Evaluate the performance of your algorithm using appropriate metrics and compare it to existing algorithms.
- Document your findings and share your algorithm with the community.
Attend a workshop on algorithmic fairness
Show steps
Stay up-to-date with the latest research and best practices in algorithmic fairness by attending relevant workshops.
Browse courses on
Fairness in Machine Learning
Show steps
Show steps
Show steps
- Research and identify workshops or conferences that focus on algorithmic fairness and ethical AI.
- Attend the workshop and actively participate in discussions, presentations, and hands-on activities.
- Network with experts and fellow attendees to gain insights and expand your knowledge.
Contribute to Fairness-Focused Open-Source Projects
Show steps
Engage with the open-source community and contribute to projects that promote fair and equitable algorithms.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source projects focused on fairness in algorithms.
- Contribute to the documentation, code, or issue tracking of these projects.
- Collaborate with other contributors to improve the quality and impact of the projects.
Career center
Learners who complete Practical Steps for Building Fair AI Algorithms will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use machine learning algorithms to make predictions about data. They must be familiar with the legal and ethical implications of algorithmic fairness, and must be skilled at designing and implementing fair algorithms. This course will provide you with the foundation you need to succeed as a Data Scientist by teaching you how to design fair algorithms and assess algorithms for fairness.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. They must have a strong understanding of the principles of fairness in AI, and must be able to apply these principles to their work. This course will provide you with the skills you need to succeed as a Machine Learning Engineer by teaching you how to design fair algorithms and assess algorithms for fairness.
Algorithm Designer
Algorithm Designer
Algorithm Designers develop and analyze algorithms to solve computational problems. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course will provide you with the foundation you need to succeed as an Algorithm Designer by teaching you how to design fair algorithms and assess algorithms for fairness.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course will provide you with the skills you need to succeed as a Data Analyst by teaching you how to design fair algorithms and assess algorithms for fairness.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course will provide you with the skills you need to succeed as a Software Engineer by teaching you how to design fair algorithms and assess algorithms for fairness.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks to businesses and organizations. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course will provide you with the skills you need to succeed as a Risk Analyst by teaching you how to design fair algorithms and assess algorithms for fairness.
Auditor
Auditor
Auditors examine and evaluate the financial and operational records of businesses and organizations. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course will provide you with the skills you need to succeed as an Auditor by teaching you how to design fair algorithms and assess algorithms for fairness.
Policy Analyst
Policy Analyst
Policy Analysts develop and analyze policies to address social and economic issues. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course will provide you with the skills you need to succeed as a Policy Analyst by teaching you how to design fair algorithms and assess algorithms for fairness.
Compliance Officer
Compliance Officer
Compliance Officers ensure that businesses and organizations comply with laws and regulations. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course will provide you with the skills you need to succeed as a Compliance Officer by teaching you how to design fair algorithms and assess algorithms for fairness.
Lawyer
Lawyer
Lawyers advise clients on legal matters and represent them in court. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course will provide you with the skills you need to succeed as a Lawyer by teaching you how to design fair algorithms and assess algorithms for fairness.
Teacher
Teacher
Teachers educate students in a variety of subjects. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course may help build a foundation for success as a Teacher by teaching you how to design fair algorithms and assess algorithms for fairness.
Consultant
Consultant
Consultants provide advice to businesses and organizations on a variety of topics. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course may help build a foundation for success as a Consultant by teaching you how to design fair algorithms and assess algorithms for fairness.
Researcher
Researcher
Researchers conduct research to advance knowledge in a variety of fields. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course may help build a foundation for success as a Researcher by teaching you how to design fair algorithms and assess algorithms for fairness.
Journalist
Journalist
Journalists investigate and report on news and current events. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course may help build a foundation for success as a Journalist by teaching you how to design fair algorithms and assess algorithms for fairness.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. They must be familiar with the principles of fairness in AI, and must be able to apply these principles to their work. This course may help build a foundation for success as an Entrepreneur by teaching you how to design fair algorithms and assess algorithms for fairness.
For more career information including salaries, visit:
OpenCourser.com/course/k30yc2/practical
Reading list
We've selected nine 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
Practical Steps for Building Fair AI Algorithms.
Will provide a comprehensive overview of the ethical considerations in designing and deploying AI algorithms, including the principles of fairness, accountability, and transparency.
Provides a practical guide to developing interpretable machine learning models, which are essential for understanding and mitigating biases in AI algorithms.
Offers a critical examination of how search engines perpetuate and reinforce racial biases, shedding light on the systemic issues in technology design and development.
More challenging but extremely foundational book that would be very good to use as a reference for those looking for a more in-depth treatment of the topics.
This classic machine learning textbook that would be helpful for providing students with the background knowledge necessary for the course.
This would be a good introductory textbook for students who are new to machine learning.
Provides a very short introduction to algorithms, which would be helpful for students who are new to this topic.
Covers a variety of advanced topics in computer vision, such as image processing, feature extraction, and object recognition.
This classic book on reinforcement learning that would be helpful for students interested in the applications of fair AI algorithms in this field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/k30yc2/practical
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Effort
4 weeks of study, 5 hours/week
Level
Introductory
Via
Coursera
Institution
Fred Hutchinson Cancer Center
Instructors
Emma Pierson
Kowe Kadoma
Language
English
Transcripts
Español
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Português
Deutsch
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中文
العربية
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Good to know
Explores issues of algorithmic fairness, which are particularly relevant in today's data-driven society
Presents ten practical principles for designing fair algorithms, making the course highly applicable
Emphasizes real-world relevance through case studies of modern algorithms, including those used in criminal justice, healthcare, and large language models like ChatGPT
Taught by Emma Pierson and Kowe Kadoma, both experienced professionals in the field of algorithmic fairness
Suitable for a broad audience, including students in high school or above with an interest in computer science and algorithm design, as well as engineers, data scientists, policy-makers, and managers
Does not require prior coding experience, making it accessible to beginners
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