Fairness and Abstraction in Sociotechnical Systems

Focus: Methods or Design

Source: Data and Society

Redability: Intermediate

Type: PDF Article

Open Source: No

SharePoint URL: https://ocaduniversity.sharepoint.com/teams/Team_WeCount/Shared%20Documents/Resources%20and%20Tools/Literature%20(curated)/Fairness%20and%20Abstraction%20in%20Sociotechnical%20Systems.pdf

Keywords: N/A

Learn Tags: Design/Methods Ethics Framework Machine Learning Research Centre

Summary: 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.