Resources
Support your learning through our searchable research library and discover valuable resources about many topics in artificial intelligence and data analytics, such as AI ethics, bias and data tools.
Select the We Count at Large tag to view a selection of speaking engagements and presentations by IDRC team members. Many of these resources showcase the efforts of IDRC Director Jutta Treviranus, whose pioneering work and insights in AI and inclusive AI continue to inspire and lead the field.
State of AI Ethics Report (SAIER) Volume 7
- Expert
MAIEI has released volume 7 of the State of AI Ethics Report (SAIER), titled "AI at the Crossroads: A Practitioner's Guide to Community-Centered Solutions." With 58 global contributors, this report assesses where AI governance stands in 2025.
Statistical Software and Blind Users
- Expert
This page was created as a vehicle for showing which statistical software could be used by blind users who rely on screen reader software to have access to printed text.
Statistical Software from a Blind Person’s Perspective
- Expert
This article shows how little is required to make R the most accessible statistical software currently available.
Stories of Transformation, an Interview with Dr. Jutta Treviranus
- Intermediate
In a special episode of the Universal Design in Life and Work podcast, Dr. Jutta Treviranus discusses inclusive design versus universal design, AI bias and non-linear logic models.
Stradigi AI
- Intermediate
A company that leverages thousands of algorithms on the market to predict, oprtimize and provide insights into what makes measureable business impacts.
Study Finds Diversity in Data Science Teams Is Key in Reducing Algorithmic Bias
- Intermediate
An analysis of a new study on sources of bias among AI developers that stresses how more diverse teams will reduce the chance for compounding biases.
Study: Only 18% of Data Science Students Are Learning about AI Ethics
- Intermediate
A summary of Anaconda's 2020 State of Data Science report with a focus on AI ethics.
Study Reveals Why AI Models That Analyze Medical Images Can Be Biased
- Intermediate
MIT researchers have found that while AI models are able to predict a patient's race, gender and age, they're also using these traits as shortcuts when making medical diagnoses.
Subtle Biases in AI Can Influence Emergency Decisions
- Intermediate
AI models used in medicine can suffer from inaccuracies and inconsistencies, in part because the data used to train the models are often not representative of real-world settings. A new MIT study assesses the impact that discriminatory AI models can have, especially for systems that are intended to provide advice in urgent situations.
Summary of the Accessible Canada Act
An overview of key provisions of the Accessible Canada Act.