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.
Fairlearn: A Toolkit for Assessing and Improving Fairness in AI
- Intermediate
Fairlearn is an open source toolkit that empowers data scientists and developers to assess and improve the fairness of their AI systems through an interactive visualization dashboard and unfairness mitigation algorithms.
Fairness and Abstraction in Sociotechnical Systems
- Expert
An article that outlines five traps that fair-ML work can fall into — framing, portability, formalism, ripple effect and solutionism — even though it tries to be more context-aware than traditional data science.
Fairness in AI Based Recruitment and Career Pathway Optimization
- Expert
This thesis examines the future of work specifically exploring the dilemma faced both by workers and organizations in an era where hiring decisions are delegated to automation. The piece further explores the biases that result and how it can be measured and suggests tools that can be deployed to mitigate the bias.
Fairness Indicators: Scalable Infrastructure for Fair ML Systems
- Expert
Google AI describes its Fairness Indicators, a suite of tools that enable regular computation and visualization of fairness metrics for binary and multi-class classification.
Fairness in Machine Learning
- Intermediate
An overview of the fairness problem in machine learning, including the cause of unfairness and possible solutions.
Fairness in Machine Learning Tutorial
- Intermediate
A NIPS 2017 tutorial that discusses the problem of machine learning fairness through three approaches: statistics, causality and measurement.
Fair Regression: Quantitative Definitions and Reduction-Based Algorithms
- Expert
As machine learning touches increasingly critical aspects of our life, including education, healthcare, criminal justice and lending, there is a growing focus on ensuring that algorithms treat various subpopulations fairly. This paper seeks to diminish this gap by developing efficient algorithms for a substantially broader set of regression tasks and model classes.
Fair, Transparent, and Accountable Algorithmic Decision-Making Processes
- Expert
This paper provides an overview of available technical solutions to enhance fairness, accountability and transparency in algorithmic decision-making and highlights the criticality of engaging multidisciplinary teams to co-develop, deploy and evaluate algorithms designed to maximize fairness and transparency.
Fairwork
Fairwork home page.
Federal Act Concerning the Protection of Personal Data (DSG)
The text of Austria's DSG and the protection of personal data.