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.
From Impoverished Intelligence to Abundant Intelligences
- Intermediate
An article by Jason Edward Lewis on how AI bias is a result of an epistemology problem, not an ethics problem, in the technology industry.
From Principles to Practice: How Can We Make AI Ethics Measurable?
- Intermediate
This working paper proposes the creation of an ethics label for AI systems that could be used by AI developers to communicate the quality of their products according to six key values: transparency, accountability, privacy, justice, reliability and environmental sustainability.
From Responsibility to Reason‑Giving Explainable Artificial Intelligence
- Expert
This paper focuses on the issue of responsibility - or the challenges of holding people morally responsible due to the opacity of AI systems.
G20: Inclusion, Equality Key in Digital Transformation
- Intermediate
During the debate, Jutta Treviranus presented the urgent need to deeply rethink inclusive participation in society. She emphasized that diversity is a valuable resource in the design of digital tools, while inclusion represents a significant challenge.
Garbage In, Garbage Out: Face Recognition on Flawed Data
- Intermediate
U.S. police departments have few rules for what images they can submit to face recognition algorithms to generate investigative leads. As a consequence, agencies can submit all manner of photos of unknown individuals for search against a police or driver licence database.
Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report
- Expert
Learn more about the current state of AI, its impacts and where the technology is moving in this report written by multidisciplinary researchers in the field.
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
- Expert
This research paper a method for evaluating gender and racial bias in AI facial analysis algorithms and datasets using the Fitzpatrick Skin Type classification system.
Gender Shades: Overview
- Intermediate
An overview of how the Gender Shades project evaluates the accuracy of AI-powered gender classification systems.
General Accessibility Improvement Framework - RGAA Version 4.1
An overview of France's General Accessibility Improvement Framework.
General Accessibility Improvement Repository (RGAA) Version 4.1
France's General Accessibility Improvement Repository (RGAA).