Bias and Discrimination in AI from edX

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!

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

Traffic lights

Read about what's good

what should give you pause

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

Reviews summary

Understanding ai bias: ethical & technical foundations

According to students, this course offers an essential and eye-opening exploration into Bias and Discrimination in AI. Learners consistently highlight the timeliness and critical importance of the topic. Many appreciate the well-structured modules and the balanced approach, covering both the social and technical dimensions of algorithmic fairness. The lectures are delivered by international experts, making complex concepts accessible. While most find it an excellent introduction that provides a strong foundational understanding, some advanced learners suggest there could be more in-depth technical demonstrations or hands-on activities in the mitigation strategies. Overall, it's widely regarded as a crucial course for professionals and anyone interested in ethical AI development.

Excellent as an introduction, but less suited for deep research.

"This course provides a solid overview, but I was hoping for more advanced mathematical or coding examples."

"As an introduction, it's fantastic, but if you're looking for deep technical dives into mitigation, it might be a bit light."

"It lays a great groundwork, though some practical applications could have been explored in greater detail."

Flexible pacing with quizzes to measure basic understanding.

"The self-paced format was perfect for my schedule, allowing me to absorb the material thoroughly."

"The quizzes were helpful for reinforcing concepts, though some felt a little too straightforward."

"I appreciated being able to watch videos at my own pace and check my understanding with the included quizzes."

Effectively bridges the gap between social and technical concepts.

"The course did a great job balancing the social aspects of bias with the underlying technical principles."

"I liked how it wasn't just technical; it covered the broader societal impacts and ethical considerations."

"It provided a comprehensive view, integrating both the human and algorithmic dimensions of fairness."

Offers actionable strategies for addressing bias and ethical development.

"I found the discussions on institutional attempts and mitigation strategies very actionable."

"The course provides concrete recommendations that I can apply in my work to guide ethical algorithm design."

"It really helped me identify sources of bias and think about practical ways to address them."

High-quality content delivered by knowledgeable international experts.

"The instructors are clearly experts in their field, providing valuable insights throughout the course."

"I really appreciated learning from international experts; their perspectives were enlightening."

"The material is well-researched and presented clearly by very knowledgeable lecturers."

Addresses a highly relevant and often overlooked area in AI.

"This course is absolutely vital for anyone involved in AI; it truly opened my eyes to the ethical dilemmas."

"I found the content incredibly important and timely, shedding light on a critical aspect of AI development."

"The subject matter is incredibly relevant in today's world, and the course explains why it's so impactful."

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

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Review your core mathematical skills before beginning the course to strengthen your base and prepare for the rigors of machine learning.

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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

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Understanding these concepts can set a strong foundation for the ethical implications of AI.

Browse courses on Bias in AI

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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

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Engaging with peers can facilitate critical thinking and the exchange of perspectives on ethical AI.

Browse courses on Ethical AI

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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

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Follow tutorials on bias mitigation in machine learning

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Explore the nuances of bias mitigation by following expert tutorials and learning about effective techniques.

Browse courses on Bias Mitigation

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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

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Put your theoretical knowledge into practice by tackling coding exercises that test your ability to identify and address bias in machine learning.

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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

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This activity helps learners develop a practical understanding of evaluating fairness in AI systems.

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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

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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

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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

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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

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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

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Through practice, learners can develop stronger implementation skills in mitigating bias in AI models.

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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

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Creating a policy helps learners apply their knowledge in a practical setting, contributing to a more responsible use of AI.

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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

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Consolidate your learning and prepare for assessments by creating a comprehensive study guide that summarizes key concepts, formulas, and examples.

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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

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Gain practical experience and contribute to real-world efforts to mitigate bias in AI by volunteering for organizations that focus on these issues.

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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 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.

See salaries and explore the career path for AI Ethicist

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.

See salaries and explore the career path for AI Policy Analyst

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.

See salaries and explore the career path for AI Researcher

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.

See salaries and explore the career path for AI Entrepreneur

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.

See salaries and explore the career path for AI Consultant

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.

See salaries and explore the career path for UX 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.

See salaries and explore the career path for UI Designer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Back-End 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.

See salaries and explore the career path for Full-Stack 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.

See salaries and explore the career path for Front-End Developer

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Product Manager