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Prospectus and limitations of algorithmic governance: an ecological evaluation of algorithmic trends

Prospectus and limitations of algorithmic governance: an ecological evaluation of algorithmic trends PurposeThe purpose of this study is to offer a roadmap for work on the ethical and societal implications of algorithms and AI. Based on an analysis of the social, technical and regulatory challenges posed by algorithmic systems in Korea, this work conducts socioecological evaluations of the governance of algorithmic transparency and accountability.Design/methodology/approachThis paper analyzes algorithm design and development from critical socioecological angles: social, technological, cultural and industrial phenomena that represent the strategic interaction among people, technology and society, touching on sensitive issues of a legal, a cultural and an ethical nature.FindingsAlgorithm technologies are a part of a social ecosystem, and its development should be based on user interests and rights within a social and cultural milieu. An algorithm represents an interrelated, multilayered ecosystem of networks, protocols, applications, services, practices and users.Practical implicationsValue-sensitive algorithm design is proposed as a novel approach for designing algorithms. As algorithms have become a constitutive technology that shapes human life, it is essential to be aware of the value-ladenness of algorithm development. Human values and social issues can be reflected in an algorithm design.Originality/valueThe arguments in this study help ensure the legitimacy and effectiveness of algorithms. This study provides insight into the challenges and opportunities of algorithms through the lens of a socioecological analysis: political discourse, social dynamics and technological choices inherent in the development of algorithm-based ecology. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Digital Policy, Regulation and Governance Emerald Publishing

Prospectus and limitations of algorithmic governance: an ecological evaluation of algorithmic trends

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
2398-5038
DOI
10.1108/DPRG-03-2019-0017
Publisher site
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Abstract

PurposeThe purpose of this study is to offer a roadmap for work on the ethical and societal implications of algorithms and AI. Based on an analysis of the social, technical and regulatory challenges posed by algorithmic systems in Korea, this work conducts socioecological evaluations of the governance of algorithmic transparency and accountability.Design/methodology/approachThis paper analyzes algorithm design and development from critical socioecological angles: social, technological, cultural and industrial phenomena that represent the strategic interaction among people, technology and society, touching on sensitive issues of a legal, a cultural and an ethical nature.FindingsAlgorithm technologies are a part of a social ecosystem, and its development should be based on user interests and rights within a social and cultural milieu. An algorithm represents an interrelated, multilayered ecosystem of networks, protocols, applications, services, practices and users.Practical implicationsValue-sensitive algorithm design is proposed as a novel approach for designing algorithms. As algorithms have become a constitutive technology that shapes human life, it is essential to be aware of the value-ladenness of algorithm development. Human values and social issues can be reflected in an algorithm design.Originality/valueThe arguments in this study help ensure the legitimacy and effectiveness of algorithms. This study provides insight into the challenges and opportunities of algorithms through the lens of a socioecological analysis: political discourse, social dynamics and technological choices inherent in the development of algorithm-based ecology.

Journal

Digital Policy, Regulation and Governance – Emerald Publishing

Published: Jun 10, 2019

References

  • Seeing without knowing: limitations of the transparency ideal and its application to algorithmic accountability
  • Towards a sociology of computational and algorithmic journalism
  • How the machine thinks: understanding opacity in machine learning algorithms
  • Algorithmic governance: developing a research agenda through the power of collective intelligence
  • Accountability in algorithmic decision making
  • Algorithmic transparency in the news media
  • What is algorithm governance?
  • Opening the government’s black boxes