Debiasing AI: Rethinking the Intersection of Innovation and Sustainability
Abstract
In an era where artificial intelligence (AI) drives unprecedented change, Debiasing AI examines the vital intersection of technology, innovation, and sustainability. This book confronts the pressing challenge of bias in AI systems, exploring its far-reaching implications for fairness, trust, and ethical practices. Through a multidisciplinary lens, the author examines how human biases are embedded in large language models, amplified by coded machine learning, and propagated through trained algorithms. Practical strategies are offered to address these issues, paving the way for the development of more equitable and inclusive AI technologies. With actionable insights, empirical case studies, and theoretical frameworks, Debiasing AI offers a roadmap for designing AI technologies that are not only innovative but also ethically sound and equitable. A must-read for scholars, industry leaders, and policymakers, this book inspires a reimagining of AI’s role in creating a fairer and more sustainable future.
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Generative AI systems have a tendency to produce hallucinated content and an inability to adequately protect personal data.
“This case underscores the serious risks posed by GAI, particularly the tendency of large language models (LLMs) to produce hallucinated content and their inability to adequately protect personal data.”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Shin 2025
One user's private information may inadvertently appear in another user's AI-generated output without consent.
“In some cases, one user's private information may inadvertently appear in another user's AI-generated output, without consent (Shin 2025).”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Shin 2025
Privacy-protective behaviors have been examined across contexts such as social media and e-commerce, typically framed as adaptive responses or avoidance strategies.
“Privacy-protective behaviors have been examined across contexts such as social media and e-commerce, typically framed as adaptive responses or avoidance strategies (Shin 2025).”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Shin 2025
Existing studies demonstrate that self-efficacy and perceived privacy risks exert direct effects on users' attitudes and verification behaviors, without requiring additional motivational explanations.
“Existing studies demonstrate that self-efficacy and perceived privacy risks exert direct effects on users' attitudes and verification behaviors, without requiring additional motivational explanations (Shin 2025).”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Shin 2025
User-provided inputs during interactions with GAI systems are typically collected and reused by default unless users actively opt out.
“Second, user-provided inputs during interactions with GAI systems are typically collected and reused by default unless users actively opt out (Shin 2025).”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Shin 2025
Trust in GAI is not grounded in confidence in system reliability but in users' willingness to compensate for known limitations through ongoing scrutiny and correction (Shin 2025).
“Trust, in this context, is not grounded in confidence in system reliability but in users' willingness to compensate for known limitations through ongoing scrutiny and correction (Shin 2025).”
From Bridging risk and retention: risk coping with privacy and hallucination in AI (2026) · cites this paper as Shin 2025
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“Algorithmic truth refers to the systematized and probabilistic processes through which AI systems render claims as fac-tual—anchored not in direct ontological correspondence, but in data-driven inference and model architecture (Shin 2025).”
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“…have suggested that blending conventional practices with digital innovations — such as open-source veri fi cation tools, collaborative platforms, and real-time monitoring systems — can enhance the credibility and responsiveness of fact-checking efforts (Brandtzaeg & Følstad, 2017; Shin, 2025).”
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Bridging risk and retention: risk coping with privacy and hallucination in AI
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“The heterogeneity in performance across models on critical thinking tasks not only reflects divergences in technical implementation but also highlights potential biases and value alignment issues in AI system development (Shin, 2025).”
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“Debiasing and accountability frameworks are critical in preventing unfair risk labeling or discriminatory intervention patterns in security contexts (Shin, 2025) [27].”
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Yin, M., & Jeon, M. (2026) · Behaviour & Information Technology · doi:10.1080/0144929x.2026.2699278
Xie, X., & Luo, H. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-08008-y
“Shin ’ s discussions of prebunking, algorithmic nudging, and inoculation suggest that anticipatory information design may be more effective than retrospective debunking in limiting the spread of extreme misunderstanding and in fl ated technological imaginaries (Shin, 2025).”
InfluentialMei, X., Li, Y., Li, J., Li, M., Sun, T.-C., & Yeung, W. (2026) · Current Psychology · doi:10.1007/s12144-026-09783-2
“To foster epistemic trust, such platforms should be designed to manage uncertainty responsibly and provide transparent, unbiased information that supports individuals’ agency and facilitates reintegration into society (Shin, 2025).”
Smart Newsroom or Structural Divide? AI Adoption and Regional Inequality in Portuguese Journalism
Vicente, P. N., Sotero, J. M., Flores, A. M. M., Ramalho, J., Granado, A., Santos-Silva, D., Burnay, C. D., Lopes, F., Camponez, C., de Miranda, J. M. S., et al. (2026) · Journalism Practice · doi:10.1080/17512786.2026.2706725
Shin, D. (2026) · Oxford University Press eBooks · doi:10.1093/9780197849644.003.0004 · Cited by 1 · see citing works
Self-citationCognitive Dissonance in Algorithmic Fact-Checking Systems
Shin, D. (2026) · doi:10.1093/9780197849644.003.0012
Self-citation
- Authors
- Shin, D.
- Corresponding author
- Shin, Donghee
- ORCID
- Shin, Donghee
- Venue
- Routledge eBooks
- Publisher
- Routledge
- Published
- 2025-03-17
- OpenAlex
- W4408519678
- Cited by
- 118
- References
- 0
- Citations by year
- 2026: 73 · 2025: 45
- FWCI
- 239.21
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Routledge eBooks (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- algorithmic bias, bias mitigation, large language models, algorithmic fairness, algorithmic trust, AI ethics, inclusive AI, sustainability
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