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.
Data Protection Laws of the World: France
An overview of how France has implemented the GDPR.
Data Protection Laws of the World: Slovenia
An overview of how Slovenia has implemented the GDPR.
Data Science, Big Data and Statistics
- Expert
An argument that traditional statistical methods were developed for small data sets and are not suitable for current large and complex data sets.
Data Science vs. Machine Learning: How Are They Different?
- Intermediate
New to the world of data science, AI and machine learning? Learn about the difference between data science and machine learning in this helpful TechTarget article.
Datasheets for Datasets
- Intermediate
An extensive list of questions for data set creators and consumers to use when creating and using data sets in order to make informed decisions and to avoid harm.
Dealing with the Incompleteness of Machine Learning
- Intermediate
An article that stresses the necessity of machine learning interpretation, where a practitioner explains and interprets a machine learning model's decisions, for countering the incompleteness of machine learning models.
Deconstructing Community-Based Collaborative Design: Towards More Equitable Participatory Design Engagement
- Expert
This article discusses the shortcomings of using a participatory model to engage marginalized groups in human-computer interaction (HCI) design and describes two case studies that show how underlying tensions between participants and researchers can undermine the purpose of the project.
Decoupled Classifiers for Group-Fair and Efficient Machine Learning
- Expert
A decoupling technique used to minimize or avoid bias and unfairness that can be added to any black-box machine learning algorithm to learn different classifier from different groups.
Decree n. 2019–536 of May 29, 2019, Taken for the Application of Law n. 78–17 of January 6, 1978, Relating to Data Processing, Files and Freedoms
The text of France's Decree n. 2019–536.
Decree No. 2019–768, Relating to the Accessibility of Online Public Communication Services for People with Disabilities
The text of France's Decree No. 2019–768.