Ethics in Linked Data
-
- Englisch ausgewählt
Fr. 79.90
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
15.07.2023
Herausgeber
Alexandra Provo + weitereVerlag
Library Juice PressSeitenzahl
492
Maße (L/B/H)
22.9/15.2/2.7 cm
Gewicht
706 g
Sprache
Englisch
ISBN
978-1-63400-133-5
This edited collection brings together contributions that explore ethics in linked data initiatives. Discussions about linked data and its potential are often utopian and technophiliac, rarely examining darker implications or harmful consequences. Since technology cannot exist outside of social, cultural, and economic spheres, it is important for creators and stewards of linked data and its related systems to recognize and address the impact (whether intended or not, positive or negative) on the communities, individuals affected. Engaging in critical and ethical analysis is ultimately an optimistic endeavor aimed at exposing problematic issues, generating best practices and guidelines, and opening up positive and generative possibilities for the implementation and use of linked data in GLAMS (Galleries, Libraries, Archives, Museums, Special Collections). The central premise of this book is that it is imperative: to foreground ethics rather than apply them as an afterthought, to acknowledge and mitigate the damage caused by existing systems, to create a place and space of justice for the minoritized, and to enable more ethical outcomes in linked data projects. This book collects the voices of practitioners, technologists, and developers working on linked data initiatives; scholars working at the intersection of ethics, cultural heritage, and technology; and workers in GLAMS, among others in order to explore emerging and changing technical and ethical landscapes. Contributions investigate the intersection of linked data with such topics as gender, indigenous knowledge, inclusive data creation, authority control, identity management, systems design, codes of ethics, sustainability, critiques of fundamental linked data models, and more.
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