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.
A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation
- Expert
This paper discusses the limitations of existing sign language data sets and how problems resulting from these limitations can be mitigated.
A Society for All: The Government’s Strategy for the Equality of Persons with Disabilities for the Period 2020–2030
The Government of Norway's strategy for equality and inclusion for people with disabilities.
Assembling Accountability: Algorithmic Impact Assessment for the Public Interest
- Expert
Explore Data & Society's report on Algorithmic Impact Assessment (AIA) as a tool for bringing accountability to the algorithmic systems that permeate everyday life. The report details what still needs to be done and why it is necessary to foster a community of committed experts and advocates.
A Survey on Bias and Fairness in Machine Learning
- Expert
A taxonomy of terms related to fairness and AI bias in machine learning, deep learning, natural language processing and algorithms.
At Senate AI Hearing, News Executives Fight against “Fair Use” Claims for AI Training Data
- Intermediate
News executives urged Congress for legal clarification that using journalism to train AI assistants like ChatGPT is not fair use and that news organizations would prefer a licencing regime for AI training content that would force Big Tech companies to pay for content.
A Tutorial on Fairness in Machine Learning
- Expert
This article highlights how unfairness in machine learning systems is mainly due to human bias existing in the training data.
Auditing Artificial Intelligence
- Intermediate
The paper provides information to auditors not only on the challenges they can anticipate in preparation for an audit, but also how they can approach the undertaking.
Auditing Employment Algorithms for Discrimination
- Intermediate
This paper presents steps toward specific standards of what constitutes an algorithmic audit and a path to enforce those with regulatory oversight.
Auditing for Algorithmic Discrimination
- Intermediate
Despite the abundance of decision-making algorithms with social impacts, this article discusses how many companies are not conducting specific audits for bias and discrimination that can help mitigate their potentially negative consequences.
Auditing the AI Auditors
- Intermediate
The writer proposes three different audit lenses as a standardized approach for assessing fairness and bias in AI systems. And with little collaboration among auditors, services that audit auditors will become necessary.