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.
Why Are Artificial Intelligence Systems Biased?
- Intermediate
An editorial that delves into societal bias in AI systems and explores how an increased awareness of bias is sparking industry changes.
Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson from an Explainable AI Competition
- Expert
The main thrust of this article is that an explainable AI model can always be constructed. Thus stakeholders should not resign themselves to black box models, particularly for high-stakes decisions.
Why Businesses Need Explainable AI—and How to Deliver It
- Expert
This article discusses the challenges of explainability and some of the ways explainable AI can benefit an organization.
Why Companies Are Thinking Twice about Using Artificial Intelligence
- Intermediate
A look at the changing views of AI technology such as facial recognition software.
Why Companies Need Artificial Intelligence Explainability
- Intermediate
This piece talks about the four characteristics of AI that makes its use unreliable and what can be done to conquer that.
Why Data Remains the Greatest Challenge for Machine Learning Projects
- Intermediate
This VentureBeat article offers a breakdown of the challenges companies face when applying machine learning in their applications and operations, including barriers to sourcing and preparing data.
Why Diversity Should Have a Critical Impact on Data Privacy
- Intermediate
This VentureBeat article looks at why diversity must play a critical role in data privacy and explores how companies can develop more inclusive and ethical technologies.
Why Does AI Have to Be Nice? Researchers Propose “Antagonistic AI”
- Intermediate
Researchers from Harvard have introduced the idea of Antagonistic AI — AI systems designed to be purposefully combative, critical and rude — challenging the paradigm of commercially popular AI.
Why Do We Keep Repeating the Same Mistakes on AI?
- Intermediate
Explore the history of AI and recurring pitfalls in its development in this Forbes article.
Why Eliminating Bias in AI Is Key to Its Success
- Intermediate
An article that explains how, with greater awareness and a purposeful approach to combating bias, AI developers have a hugely influential role to play in establishing a more fair and just society.