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.
WHO Calls for Safe and Ethical AI for Health
- Intermediate
The WHO is calling for caution to be exercised in using AI-generated LLMs to protect and promote human well-being, safety, and autonomy, and to preserve public health.
Who Is Responsible for Biased and Intrusive Algorithms
- Intermediate
While algorithms often make our lives more efficient, the same algorithms frequently violate our privacy and are biased and discriminatory. In the book The Ethical Algorithm, the authors suggest that the solution is to embed precise definitions of fairness, accuracy, transparency and ethics at the algorithm’s design stage.
WHO Releases AI Ethics and Governance Guidance for Large Multi-modal Models
- Intermediate
The WHO has released new guidance on the ethics and governance of large multi-modal models (LMMs) – a type of fast-growing generative AI technology with applications across health care. The guidance outlines over 40 recommendations for consideration by governments, technology companies, and health care providers.
Who’s Watching? What You Need to Know About Personal Data Security
- Beginner
A helpful introduction to current issues with data security, data collection and consent that was written by Encode Justice Canada and published by MAIEI.
Why AI Bias Can’t Be Solved with More AI
- Intermediate
An interview with Alejandro Saucedo on his belief that reintroducing human experise, instead of more technology, can prevent AI bias.
Why AI Fairness Conversations Must Include Disabled People
- Intermediate
Part of a series of Harvard Gazette articles on non-apparent disabilities, this entry stresses the importance of including people with disabilities in the decision-making and development processes for AI.
Why AI Fairness Tools Might Actually Cause More Problems
- Intermediate
The process of codifying a rule, commonly used to measure impacts on protected groups in hiring, for AI fairness tools has, in some instances, caused an inverse reaction due to a removal of the human element of decision-making.
Why AI Governance Is Important for Building More Trustworthy, Explainable AI
- Intermediate
AI governance is being encouraged, which entrenches accountability when it comes to developing and implementing AI in organizations.
Why AI Is a Know-It-All Know Nothing
- Intermediate
Are LLMs trustworthy or are they systematically deceptive? Learn more in this VentureBeat article.
Why Algorithmic Auditing Can’t Fully Cope with AI Bias in Hiring
- Intermediate
Here the writer point of the challenges of algorithmic bias and the suspicion surrounding its audit.