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.
Basic Policy on Economic and Fiscal Management and Reform 2020
Outline of the Government of Japan's Basic Policy on Economic and Fiscal Management and Reform 2020.
Before Worrying about AI’s Threat to Humankind, Here’s What Else Canada Can Do
- Intermediate
An open letter critical of Bill C27, the federal government's proposed legislation on AI, argues that the bill's Artificial Intelligence and Data Act is lacking details, leaving many important AI-related rules to be decided after the law is passed.
Berlin Declaration Digital Society and Value-Based Digital Government
This declaration aims to contribute to a value-based digital transformation by addressing and ultimately strengthening digital participation and digital inclusion.
Better Decision Support through Exploratory Discrimination-Aware Data Mining: Foundations and Empirical Evidence
- Expert
This paper describes an exploratory study that uses different discrimination-aware data mining (DADM) methods to determine how information technology supports the decision process and keep it free from bias.
Beware the Emergence of Shadow AI
- Intermediate
As organizations move to deploy dozens of AI products and services to accelerate their workflows and gain productivity, an upsurge of unreported and unsanctioned generative AI use has brought forth the next iteration of the classic “Shadow IT“ problem: Shadow AI.
Beyond a Human Rights–Based Approach to AI Governance: Promise, Pitfalls, Plea
- Expert
This article argues that human rights can function as a much sought after moral compass to constitute the basis of an AI governance framework and draws attention to the socio-ethical implications of these systems, including their potential to both generate economic and societal benefits and cause significant harm.
Beyond Bias and Discrimination: Redefining the AI Ethics Principle of Fairness in Healthcare ML Algorithms
- Expert
This paper shows how an ethical inquiry into the concept of fairness helps highlight shortcomings in the current conceptualization of fairness in healthcare ML algorithms.
Biased AI Is Another Sign We Need to Solve the Cybersecurity Diversity Problem
- Intermediate
This article highlights three ways that cybersecurity’s diversity problem is linked to biased AI. Cognitive diversity can contribute to the production of fair algorithms, help curate balanced training data and enable the supervision of secure AI.
Biased Programmers? Or Biased Data? A Field Experiment in Operationalizing AI Ethics
- Expert
A study about the sources of bias among AI developers that stresses how more diverse teams will reduce the chance for compounding biases.
Bias in Automated Speaker Recognition
- Expert
A recent paper on bias in automated speaker recognition software reveals that not only how prone to bias the technology is, but its performance is heavily impacted by the speaker's demographic attributes.