株式会社極東書店トップ商品一覧Human-AI Collaboration in Research: Practical Applications, Ethical Frameworks, and Future Directions.

商品詳細

Human-AI Collaboration in Research: Practical Applications, Ethical Frameworks, and Future Directions.

Human-AI Collaboration in Research: Practical Applications, Ethical Frameworks, and Future Directions.

・ISBN 978-1-041-33163-6 hard GB£ 67.99

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お気に入り
電子版あり 大学・学術機関向け電子ブック(eBook)ISBN 9781003786405
著者・編者Skubis, Ida / Xerri, Daniel / Adamovic, Mladen,
出版社 (CRC Press, UK)
出版年月2026
ページ数74 pp.
言語ENG
ニュース番号<A05-34739>

解説

Human-AI Collaboration in Research: Practical Applications, Ethical Frameworks, and Future Directions positions human-AI collaboration (HAIC) as a defining feature of contemporary research ecosystems. The book examines the incorporation of AI across the research lifecycle, including research design, literature work, data collection and processing, analysis, interpretation, academic writing, and dissemination. It highlights the opportunities created by automation and generative systems, alongside the challenges raised for research integrity, accountability, transparency, privacy, and epistemic authority. Ethical and regulatory foundations are addressed through established frameworks such as the Belmont Report and the Declaration of Helsinki, as well as European governance instruments including the Ethics Guidelines for Trustworthy AI, the General Data Protection Regulation, and the EU Artificial Intelligence Act. By combining interdisciplinary perspectives from robotics, management research, and education, the volume translates abstract ethical principles into concrete research-relevant practices, offering a coherent and human-centred approach to trustworthy AI-supported inquiry.

The book offers original, research-ready frameworks and applied guidance for responsible HAIC, combining real-world case studies with practical strategies for trustworthy AI use. It equips researchers, educators, and management scholars with tools for human oversight, transparency, bias mitigation, and accountable AI-supported workflows, ensuring scientific rigour alongside innovation.