Toward Fair, Accountable, and Transparent Algorithms: Case Studies on Algorithm Initiatives in Korea and China
Abstract
Algorithms are playing an increasingly huge role becoming a big part of human lives. With the conceptualisation of algorithms as a socio-technical system, this study investigates algorithm initiatives in Korea and China in terms of the opportunities, risks, and challenges embedded in their development. This study analyses algorithm development and trends from a critical socio-technical lens: social, technological, cultural, and industrial phenomena that represent strategic interactions involving people, technology, and society and elicit sensitive legal, cultural, and ethical rhetoric issues. Despite rosy predictions and proactive drives, new risks related to privacy, transparency, and fairness emerge as critical concerns of the social ramifications of algorithms and of their impacts on the new information milieu. With these emerging issues, questions are raised on the ways to govern algorithms and to respond to potential outcomes that such a policy approach may have on society and industry. Both Korea and China will likely struggle with the social cost of AI as it challenges what it means to be fair, transparent, and accountable. The socio-political implications of algorithms are discussed to identify key issues as both countries progress toward an algorithm-based, AI-driven society.
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Open datasets such as ImageNet and Open Images have glaring gaps in geo-diversity.
“For instance, [190] highlights the glaring gaps in geo-diversity of open datasets such as ImageNet and Open Images that drive much of the computer vision research.”
From Re-imagining Algorithmic Fairness in India and Beyond (2021) · cites this paper as [190]
Representational gaps in data are documented as a major challenge for responsible AI from a global perspective.
“Representational gaps in data is documented as one of the major challenges in achieving responsible AI from a global perspective [21,190].”
From Re-imagining Algorithmic Fairness in India and Beyond (2021) · cites this paper as [190]
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- Authors
- Shin, D. (.
- Affiliations
- Zayed University (AE)
- Corresponding author
- Shin, Donghee (Don)
- ORCID
- Shin, Donghee (Don)
- Venue
- Javnost - The Public
- Publisher
- Informa UK Limited
- Published
- 2019-05-23
- Volume
- 26
- Issue
- 3
- Pages
- 274-290
- ISSN
- 1318-3222 (ISSN-L)
- 1854-8377
- OpenAlex
- W2944818567
- Cited by
- 53
- References
- 22
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 13 · 2023: 8 · 2022: 7 · 2021: 6 · 2020: 8 · 2019: 3
- FWCI
- 10.49
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Javnost - The Public (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Political Science and International Relations › E-Government and Public Services
- Keywords
- algorithmic governance, algorithmic fairness, algorithmic transparency, algorithmic accountability, AI policy, socio-technical systems, privacy risks, algorithmic risk, Korea, China
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