An Introduction to Algorithmic Fairness
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 2.
4 papers.
- 2024The Sociolinguistic Foundations of Language Modelingcited by 13
- 2023One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisionscited by 12
- 2022The Fairness Field Guide: Perspectives from Social and Formal Sciencescited by 3
- 2024Rolling in the deep of cognitive and AI biasescited by 2
- 2024Rolling in the deep of cognitive and AI biases30 Jul 2024
- 2024The Sociolinguistic Foundations of Language Modeling12 Jul 2024
- 2023One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions31 Aug 2023
- 2022The Fairness Field Guide: Perspectives from Social and Formal Sciences13 Jan 2022