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Golnoosh Farnadi,
Emre Kiciman,
and
Rachel Thomas
Engage in this course pertaining to a highly impactful yet, too rarely discussed, AI-related topic. You will learn from international experts in the field, also speakers at IVADO’s International School on Bias and Discrimination in AI, which took place in Montreal, and explore the social and technical aspects of bias, discrimination and fairness in machine learning and algorithm design.
Read more
Engage in this course pertaining to a highly impactful yet, too rarely discussed, AI-related topic. You will learn from international experts in the field, also speakers at IVADO’s International School on Bias and Discrimination in AI, which took place in Montreal, and explore the social and technical aspects of bias, discrimination and fairness in machine learning and algorithm design.
Read more
Engage in this course pertaining to a highly impactful yet, too rarely discussed, AI-related topic. You will learn from international experts in the field, also speakers at IVADO’s International School on Bias and Discrimination in AI, which took place in Montreal, and explore the social and technical aspects of bias, discrimination and fairness in machine learning and algorithm design.
The main focus of this course is: gender, race and socioeconomic-based bias as well as bias in data-driven predictive models leading to decisions. The course is primarily intended for professionals and academics with basic knowledge in mathematics and programming, but the rich content will be of great use to whomever uses, or is interested in, AI in any other way. These sociotechnical topics have proven to be great eye-openers for technical professionals!
The total duration of the video content available in this course is 7:30 hours, cut into relevant segments that you may watch at your own pace. There are also comprehensive quizzes at the end of each segment to measure your understanding of the content.
IVADO is a scientific and economic data science hub bridging industrial, academic and governmental partners with expertise in digital intelligence. One of its missions is to contribute to the advancement of digital knowledge and train new generations of bias-aware data scientists.
Welcome to this enlightening journey in the world of ethical AI!
Register for this course and see more details by visiting:
OpenCourser.com/course/982q56/bias
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What's inside
Learning objectives
- Understanding bias and discrimination in all its aspects
- Exploring the harmful effects of bias in machine learning (discriminatory effects of algorithmic decision-making)
- Identifying the sources of bias and discrimination in machine learning
- Mitigating bias in machine learning (strategies for addressing bias)
- Recommendations to guide the ethical development and evaluation of algorithms
- Understanding bias and discrimination in all its aspects
- Exploring the harmful effects of bias in machine learning (discriminatory effects of algorithmic decision-making)
- Identifying the sources of bias and discrimination in machine learning
- Mitigating bias in machine learning (strategies for addressing bias)
- Recommendations to guide the ethical development and evaluation of algorithms
Syllabus
Module 1 The concepts of bias and fairness in AI
Different Types of Bias
Fairness criteria and metrics
Module 2 Fields where problems were diagnosed
Read more
Syllabus
Module 1 The concepts of bias and fairness in AI
Different Types of Bias
Fairness criteria and metrics
Module 2 Fields where problems were diagnosed
Read more
Privacy, labour and legal system
Public policy and Health
Module 3 Institutional attempts to mitigate bias and discrimination in AI
Canada's Algorithmic Impact Assessment Framework
The Montreal Declaration for Responsible AI
Module 4 Technical attempts to mitigate bias and discrimination in AI
Fairness constraints in graph embeddings
Gender bias in text
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores bias in AI, a pressing ethical issue often overlooked
Taught by recognized experts in the field of AI bias
Certified by IVADO, a notable scientific hub in data science
Covers essential topics like fairness criteria, privacy, and institutional responses
Provides guidance on ethical AI development and evaluation
Includes technical approaches to mitigate AI bias, such as fairness constraints and gender bias analysis
Save this course
Save Bias and Discrimination in AI to your list so you can find it easily later:
Save
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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 Bias and Discrimination in AI with these
activities:
Refresh foundational mathematical skills
Show steps
Review your core mathematical skills before beginning the course to strengthen your base and prepare for the rigors of machine learning.
Browse courses on
Statistics
Show steps
-
Review your notes or reference materials on the key mathematical concepts
-
Complete practice problems and exercises to test your understanding
-
Consider using online resources or textbooks to supplement your studies
Understand Key Concepts of Bias and Algorithm Design
Show steps
Understanding these concepts can set a strong foundation for the ethical implications of AI.
Browse courses on
Bias in AI
Show steps
-
Review papers or articles on bias in AI.
-
Engage in online discussions or forums related to bias in machine learning.
-
Attend a lecture or workshop on the topic.
Join a Study Group for Ethical AI
Show steps
Engaging with peers can facilitate critical thinking and the exchange of perspectives on ethical AI.
Browse courses on
Ethical AI
Show steps
-
Find a study group focused on ethical AI.
-
Participate in discussions on case studies related to bias in AI.
-
Collaborate on projects or presentations on ethical AI principles.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Follow tutorials on bias mitigation in machine learning
Show steps
Explore the nuances of bias mitigation by following expert tutorials and learning about effective techniques.
Browse courses on
Bias Mitigation
Show steps
-
Identify reputable online platforms or courses offering tutorials on bias mitigation
-
Choose tutorials that align with the course syllabus and your learning goals
-
Follow the tutorials diligently, taking notes and practicing the concepts
-
Engage with the tutorial forums or discussion groups to seek clarification and share insights
Complete coding exercises on bias identification and mitigation
Show steps
Put your theoretical knowledge into practice by tackling coding exercises that test your ability to identify and address bias in machine learning.
Show steps
-
Find online platforms or coding challenges that provide exercises on bias identification and mitigation
-
Select exercises that cover various techniques and scenarios related to bias
-
Code solutions to the exercises, testing different approaches and evaluating their effectiveness
-
Compare your solutions with others and seek feedback to refine your understanding
Practice Assessing Algorithmic Fairness
Show steps
This activity helps learners develop a practical understanding of evaluating fairness in AI systems.
Show steps
-
Complete tutorials on fairness metrics for machine learning models.
-
Use online tools or libraries for assessing algorithmic fairness.
-
Analyze real-world case studies of algorithmic bias.
Participate in study groups or discussion forums
Show steps
Engage with peers to exchange ideas, clarify concepts, and broaden your perspective on the social and ethical implications of bias in AI.
Browse courses on
Bias in AI
Show steps
-
Join study groups or online discussion forums related to bias in AI
-
Participate actively in discussions, sharing your insights and asking questions
-
Collaborate on projects or assignments that explore different aspects of bias
-
Seek feedback and support from your peers to enhance your learning
Develop a blog post or presentation on a topic related to bias in AI
Show steps
Deepen your understanding of bias in AI by creating a blog post or presentation that shares your insights and perspectives on a specific topic.
Browse courses on
Bias in AI
Show steps
-
Choose a specific topic related to bias in AI that you are interested in exploring
-
Research and gather information from credible sources
-
Organize and structure your content in a logical and engaging way
-
Create the blog post or presentation using appropriate tools and formats
-
Share your work with others and invite feedback to improve its quality
Solve Coding Challenges on Bias Mitigation
Show steps
Through practice, learners can develop stronger implementation skills in mitigating bias in AI models.
Show steps
-
Solve coding exercises on bias mitigation.
-
Implement bias mitigation techniques in personal projects.
-
Participate in online coding competitions related to bias mitigation.
Develop a Proposal for an Ethical AI Policy
Show steps
Creating a policy helps learners apply their knowledge in a practical setting, contributing to a more responsible use of AI.
Browse courses on
AI Governance
Show steps
-
Research existing ethical AI frameworks and guidelines.
-
Identify specific areas and concerns within your organization or domain.
-
Draft a policy proposal outlining principles, implementation strategies, and evaluation methods.
Create a comprehensive study guide or cheat sheet
Show steps
Consolidate your learning and prepare for assessments by creating a comprehensive study guide that summarizes key concepts, formulas, and examples.
Browse courses on
Study Guide
Show steps
-
Gather all your notes, lecture materials, and other resources
-
Identify and organize the most important concepts, definitions, and theorems
-
Condense the information into a concise and well-structured format
-
Use colors, diagrams, and visual elements to enhance the clarity and memorability of the study guide
Volunteer for organizations working on bias mitigation in AI
Show steps
Gain practical experience and contribute to real-world efforts to mitigate bias in AI by volunteering for organizations that focus on these issues.
Show steps
-
Research and identify organizations that work on bias mitigation in AI
-
Contact the organizations and inquire about volunteer opportunities
-
Attend training and orientations provided by the organization
-
Contribute your skills and knowledge to the organization's projects and initiatives
Refresh foundational mathematical skills
Show steps
Review your core mathematical skills before beginning the course to strengthen your base and prepare for the rigors of machine learning.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review your notes or reference materials on the key mathematical concepts
- Complete practice problems and exercises to test your understanding
- Consider using online resources or textbooks to supplement your studies
Understand Key Concepts of Bias and Algorithm Design
Show steps
Understanding these concepts can set a strong foundation for the ethical implications of AI.
Browse courses on
Bias in AI
Show steps
Show steps
Show steps
- Review papers or articles on bias in AI.
- Engage in online discussions or forums related to bias in machine learning.
- Attend a lecture or workshop on the topic.
Join a Study Group for Ethical AI
Show steps
Engaging with peers can facilitate critical thinking and the exchange of perspectives on ethical AI.
Browse courses on
Ethical AI
Show steps
Show steps
Show steps
- Find a study group focused on ethical AI.
- Participate in discussions on case studies related to bias in AI.
- Collaborate on projects or presentations on ethical AI principles.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Follow tutorials on bias mitigation in machine learning
Show steps
Explore the nuances of bias mitigation by following expert tutorials and learning about effective techniques.
Browse courses on
Bias Mitigation
Show steps
Show steps
Show steps
- Identify reputable online platforms or courses offering tutorials on bias mitigation
- Choose tutorials that align with the course syllabus and your learning goals
- Follow the tutorials diligently, taking notes and practicing the concepts
- Engage with the tutorial forums or discussion groups to seek clarification and share insights
Complete coding exercises on bias identification and mitigation
Show steps
Put your theoretical knowledge into practice by tackling coding exercises that test your ability to identify and address bias in machine learning.
Show steps
Show steps
Show steps
- Find online platforms or coding challenges that provide exercises on bias identification and mitigation
- Select exercises that cover various techniques and scenarios related to bias
- Code solutions to the exercises, testing different approaches and evaluating their effectiveness
- Compare your solutions with others and seek feedback to refine your understanding
Practice Assessing Algorithmic Fairness
Show steps
This activity helps learners develop a practical understanding of evaluating fairness in AI systems.
Show steps
Show steps
Show steps
- Complete tutorials on fairness metrics for machine learning models.
- Use online tools or libraries for assessing algorithmic fairness.
- Analyze real-world case studies of algorithmic bias.
Participate in study groups or discussion forums
Show steps
Engage with peers to exchange ideas, clarify concepts, and broaden your perspective on the social and ethical implications of bias in AI.
Browse courses on
Bias in AI
Show steps
Show steps
Show steps
- Join study groups or online discussion forums related to bias in AI
- Participate actively in discussions, sharing your insights and asking questions
- Collaborate on projects or assignments that explore different aspects of bias
- Seek feedback and support from your peers to enhance your learning
Develop a blog post or presentation on a topic related to bias in AI
Show steps
Deepen your understanding of bias in AI by creating a blog post or presentation that shares your insights and perspectives on a specific topic.
Browse courses on
Bias in AI
Show steps
Show steps
Show steps
- Choose a specific topic related to bias in AI that you are interested in exploring
- Research and gather information from credible sources
- Organize and structure your content in a logical and engaging way
- Create the blog post or presentation using appropriate tools and formats
- Share your work with others and invite feedback to improve its quality
Solve Coding Challenges on Bias Mitigation
Show steps
Through practice, learners can develop stronger implementation skills in mitigating bias in AI models.
Show steps
Show steps
Show steps
- Solve coding exercises on bias mitigation.
- Implement bias mitigation techniques in personal projects.
- Participate in online coding competitions related to bias mitigation.
Develop a Proposal for an Ethical AI Policy
Show steps
Creating a policy helps learners apply their knowledge in a practical setting, contributing to a more responsible use of AI.
Browse courses on
AI Governance
Show steps
Show steps
Show steps
- Research existing ethical AI frameworks and guidelines.
- Identify specific areas and concerns within your organization or domain.
- Draft a policy proposal outlining principles, implementation strategies, and evaluation methods.
Create a comprehensive study guide or cheat sheet
Show steps
Consolidate your learning and prepare for assessments by creating a comprehensive study guide that summarizes key concepts, formulas, and examples.
Browse courses on
Study Guide
Show steps
Show steps
Show steps
- Gather all your notes, lecture materials, and other resources
- Identify and organize the most important concepts, definitions, and theorems
- Condense the information into a concise and well-structured format
- Use colors, diagrams, and visual elements to enhance the clarity and memorability of the study guide
Volunteer for organizations working on bias mitigation in AI
Show steps
Gain practical experience and contribute to real-world efforts to mitigate bias in AI by volunteering for organizations that focus on these issues.
Show steps
Show steps
Show steps
- Research and identify organizations that work on bias mitigation in AI
- Contact the organizations and inquire about volunteer opportunities
- Attend training and orientations provided by the organization
- Contribute your skills and knowledge to the organization's projects and initiatives
Career center
Learners who complete Bias and Discrimination in AI will develop knowledge and skills
that may be useful to these careers:
AI Researcher
AI Researcher
AI Researchers develop new algorithms and techniques for artificial intelligence. This course provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models. This course would be particularly relevant to a career as an AI Researcher because it provides a comprehensive understanding of the technical challenges facing artificial intelligence.
AI Consultant
AI Consultant
AI Consultants advise organizations on how to use artificial intelligence to achieve their business goals. This course provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models. This course would be particularly relevant to a career as an AI Consultant because it provides a comprehensive understanding of the challenges and opportunities facing organizations in the age of artificial intelligence.
AI Ethicist
AI Ethicist
AI Ethicists are responsible for ensuring that artificial intelligence is developed and used in a responsible and ethical manner. This course provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models. This course would be particularly relevant to a career as an AI Ethicist because it provides a comprehensive understanding of the ethical challenges posed by artificial intelligence.
AI Entrepreneur
AI Entrepreneur
AI Entrepreneurs develop and launch new businesses based on artificial intelligence. This course provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models. This course would be particularly relevant to a career as an AI Entrepreneur because it provides a comprehensive understanding of the business landscape surrounding artificial intelligence.
AI Policy Analyst
AI Policy Analyst
AI Policy Analysts develop and implement policies related to artificial intelligence. This course provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models. This course would be particularly relevant to a career as an AI Policy Analyst because it provides a comprehensive understanding of the policy landscape surrounding artificial intelligence.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. This course may be useful in a career as a Product Manager because it provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models.
UX Designer
UX Designer
UX Designers design and evaluate user interfaces. This course may be useful in a career as a UX Designer because it provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, analyzing, and interpreting data. This course may be useful in a career as a Data Scientist because it provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models.
Back-End Developer
Back-End Developer
Back-End Developers design and develop the server-side of websites and applications. This course may be useful in a career as a Back-End Developer because it provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models.
UI Designer
UI Designer
UI Designers design and develop user interfaces. This course may be useful in a career as a UI Designer because it provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models.
Front-End Developer
Front-End Developer
Front-End Developers design and develop the user-facing side of websites and applications. This course may be useful in a career as a Front-End Developer because it provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course may be useful in a career as a Machine Learning Engineer because it provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course may be useful in a career as a Software Engineer because it provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course may be useful in a career as a Data Analyst because it provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models.
Full-Stack Developer
Full-Stack Developer
Full-Stack Developers design and develop both the front-end and back-end of websites and applications. This course may be useful in a career as a Full-Stack Developer because it provides an overview of the ethical and social implications of artificial intelligence, as well as the technical skills needed to mitigate bias and discrimination in machine learning models.
For more career information including salaries, visit:
OpenCourser.com/course/982q56/bias
Reading list
We've selected eight 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
Bias and Discrimination in AI.
Examines the ways in which search engines perpetuate racism and other forms of discrimination.
Provides a philosophical and ethical framework for thinking about the development and use of algorithms.
Provides a detailed examination of the ways in which AI is used to automate discrimination and inequality in the criminal justice system.
Save
Provides a comprehensive overview of the latest research on intelligence and machine learning, including the challenges of bias and discrimination.
Provides a more in-depth look at the theory and algorithms of machine learning.
Provides a practical guide to using R for machine learning.
Provides a practical guide to using Python for machine learning.
Provides a gentle introduction to the field of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/982q56/bias
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Effort
4 to 6 hours per week for 4 weeks
Level
Intermediate
Via
edX
Institution
Université de Montréal
Instructors
Golnoosh Farnadi
Emre Kiciman
Rachel Thomas
Language
English
Transcript
English
Good to know
Explores bias in AI, a pressing ethical issue often overlooked
Taught by recognized experts in the field of AI bias
Certified by IVADO, a notable scientific hub in data science
Covers essential topics like fairness criteria, privacy, and institutional responses
Provides guidance on ethical AI development and evaluation
Includes technical approaches to mitigate AI bias, such as fairness constraints and gender bias analysis
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