Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/233110
Authors: 
Simon, Judith
Wong, Pak Hang
Rieder, Gernot
Year of Publication: 
2020
Citation: 
[Journal:] Internet Policy Review [ISSN:] 2197-6775 [Volume:] 9 [Year:] 2020 [Issue:] 4 [Pages:] 1-16
Abstract: 
Recently, amid growing awareness that computer algorithms are not neutral tools but can cause harm by reproducing and amplifying bias, attempts to detect and prevent such biases have intensified. An approach that has received considerable attention in this regard is the Value Sensitive Design (VSD) methodology, which aims to contribute to both the critical analysis of (dis)values in existing technologies and the construction of novel technologies that account for specific desired values. This article provides a brief overview of the key features of the Value Sensitive Design approach, examines its contributions to understanding and addressing issues around bias in computer systems, outlines the current debates on algorithmic bias and fairness in machine learning, and discusses how such debates could profit from VSD-derived insights and recommendations. Relating these debates on values in design and algorithmic bias to research on cognitive biases, we conclude by stressing our collective duty to not only detect and counter biases in software systems, but to also address and remedy their societal origins.
Subjects: 
Value sensitive design
Algorithmic bias
Human values
Fairness
Fairness in Machine Learning
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/3.0/de/deed.de
Document Type: 
Article

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.