EdX

Bias and Discrimination in AI (edX)

Offered by Université de Montréal, UMontrealX,
Bias and Discrimination in AI (edX)

Discover how even computer algorithms may be biased and have a serious impact on our every day lives. In this MOOC, based on an IVADO School involving various international experts in the field, you will learn how to identify and alleviate bias and discrimination in Artificial Intelligence.

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

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 you'll learn

  • 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 2Fields where problems were diagnosed

  • Privacy, labour and legal system
  • Public policy and Health

Module 3Institutional attempts to mitigate bias and discrimination in AI

  • Canada's Algorithmic Impact Assessment Framework
  • The Montreal Declaration for Responsible AI

Module 4Technical attempts to mitigate bias and discrimination in AI

  • Fairness constraints in graph embeddings
  • Gender bias in text
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Fundamentals of TinyML (edX) EdX
HarvardX,Harvard University

Fundamentals of TinyML (edX)

Statistics & Data Analysis Data Science

Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML. What do you know about TinyML? Tiny Machine Learning (TinyML) is one of the fastest-growing areas of Deep Learning and is rapidly becoming more accessible. This course provides a foundation for you to understand this emerging field.

Self Paced
Self-Paced
Machine Learning Embedded Systems Data Science
Details
CS50's Introduction to Artificial Intelligence with Python (edX) EdX
HarvardX,Harvard University

CS50's Introduction to Artificial Intelligence with Python (edX)

Robotics & Computer Vision

Learn to use machine learning in Python in this introductory course on artificial intelligence. AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly. This course will enable you to take the first step toward solving important real-world problems and future-proofing your career.

Self Paced
Self-Paced
Python Artificial Intelligence Machine Learning
Details
Data Science: R Basics (edX) EdX
HarvardX,Harvard University

Data Science: R Basics (edX)

Statistics & Data Analysis Data Science

Build a foundation in R and learn how to wrangle, analyze, and visualize data. This course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you’ll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.

Self Paced
Self-Paced
Probability Machine Learning Inference
Details
Visualización de Datos y Storytelling (edX) EdX
Tecnológico de Monterrey,TecdeMonterreyX

Visualización de Datos y Storytelling (edX)

Engineering Statistics & Data Analysis

Aprende en este curso en línea que es la visualización de datos, sus usos; los elementos que la conforman y la forma de poder utilizarla para el apoyo en la toma de las mejores decisiones para las empresas basadas en el análisis de datos. Digamos que necesitas comprender big data; miles o incluso millones de filas de datos, y tienes poco tiempo para hacerlo. los datos pueden provenir de tu equipo, en cuyo caso tal vez ya estés familiarizado con lo que estás midiendo y de los resultados que se esperan. O puede provenir de otro equipo, o tal vez de varios equipos a la vez, y estar completamente familiarizado.

Self Paced
Self-Paced
Big Data Storytelling Data Science
Details
Python for Data Science (edX) EdX
University of California, San Diego,UC San DiegoX

Python for Data Science (edX)

Statistics & Data Analysis CS: Programming

Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets. In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?

Self Paced
Self-Paced
Programming Python Machine Learning
Details
Data Science: Capstone (edX) EdX
HarvardX,Harvard University

Data Science: Capstone (edX)

Statistics & Data Analysis Data Science

Show what you’ve learned from the Professional Certificate Program in Data Science. To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.

Self Paced
Self-Paced
Probability Machine Learning Regression
Details
Data Science Ethics (edX) EdX
University of Michigan,MichiganX

Data Science Ethics (edX)

CS: Software Engineering Engineering

Learn how to think through the ethics surrounding privacy, data sharing, and algorithmic decision-making. As patients, we care about the privacy of our medical record; but as patients, we also wish to benefit from the analysis of data in medical records. As citizens, we want a fair trial before being punished for a crime; but as citizens, we want to stop terrorists before they attack us. As decision-makers, we value the advice we get from data-driven algorithms; but as decision-makers, we also worry about unintended bias.

Self Paced
Self-Paced
Ethics Decision Making Privacy
Details
Dynamic Programming: Applications In Machine Learning and Genomics (edX) EdX
University of California, San Diego,UC San DiegoX

Dynamic Programming: Applications In Machine Learning and Genomics (edX)

Sci: Biology & Life Sciences Sci: Mathematics

Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution. If you look at two genes that serve the same purpose in two different species, how can you rigorously compare these genes in order to see how they have evolved away from each other?

Self Paced
Self-Paced
Machine Learning Genomics Dynamic Programming
Details
PyTorch Basics for Machine Learning (edX) EdX
IBM

PyTorch Basics for Machine Learning (edX)

Statistics & Data Analysis Data Science

This course is the first part in a two part course and will teach you the fundamentals of PyTorch. In this course you will implement classic machine learning algorithms, focusing on how PyTorch creates and optimizes models. You will quickly iterate through different aspects of PyTorch giving you strong foundations and all the prerequisites you need before you build deep learning models.

Self Paced
Self-Paced
ML Machine Learning Linear Regression
Details
Data Analytics Basics for Everyone (edX) EdX
IBM

Data Analytics Basics for Everyone (edX)

Statistics & Data Analysis

Learn the fundamentals of Data Analytics and gain an understanding of the data ecosystem, the process and lifecycle of data analytics, career opportunities, and the different learning paths you can take to be a Data Analyst. In this course, you will learn about the various components of a modern data ecosystem and the role Data Analysts, Data Scientists, and Data Engineers play in this ecosystem.

Self Paced
Self-Paced
Big Data Data Science Data Scientist
Details