An Introduction to Algorithmic Fairness
Hilde J.P. Weerts

TL;DR
This paper introduces core concepts of algorithmic fairness, discussing types of fairness-related harms, main fairness notions, and underlying biases in machine learning systems.
Contribution
It provides an accessible overview of key ideas and frameworks in algorithmic fairness research, highlighting the connection between biases and fairness issues.
Findings
Different types of fairness-related harms identified
Two main notions of algorithmic fairness explained
Biases in machine learning linked to fairness concerns
Abstract
In recent years, there has been an increasing awareness of both the public and scientific community that algorithmic systems can reproduce, amplify, or even introduce unfairness in our societies. These lecture notes provide an introduction to some of the core concepts in algorithmic fairness research. We list different types of fairness-related harms, explain two main notions of algorithmic fairness, and map the biases that underlie these harms upon the machine learning development process.
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Taxonomy
TopicsEthics and Social Impacts of AI · Artificial Intelligence in Healthcare and Education · Neuroethics, Human Enhancement, Biomedical Innovations
