This book offers a clear, concise introduction to trustworthy AI, treating AI not just as a technical artifact but as a socio-technical system embedded in human contexts, designed for teaching and learning in computer science, data science, law, policy, business, and related fields.
Abstract
Artificial intelligence is reshaping decisions that affect people, institutions, and societies. Understanding how to design, deploy, and govern AI systems that can be trusted is now essential in many disciplines. This book offers a clear, concise introduction to trustworthy AI, treating AI not just as a technical artifact but as a socio-technical system embedded in human contexts. Developed from an internationally applicable educational framework, the book is designed for teaching and learning in computer science, data science, law, policy, business, and related fields. It equips students and professionals with the concepts and judgment needed to engage critically and responsibly with AI in practice. Combining ethics, governance, and practical insight, the book explains key concepts including transparency, fairness, accountability, human oversight, and stakeholder participation. An interdisciplinary approach makes the material accessible to both technical and non-technical audiences, with realistic scenarios and reflection questions so readers connect principles to real-world AI applications.
This conceptual and normative paper links together research on anthropomorphism, mental models, trust calibration and AI-assisted decision-making into a single end-to-end chain, proposing a conceptual model with propositions for empirical testing.
The Stoic concept of the hegemonikon—the capacity for assent that directs the mind—is used as a framework for understanding intellectual creation and decision-making, clarifying when AI supports human judgment and when it replaces decision-making authority.
Christos A. Koutsotasios, Elias Vavouras· Dianoesis· 0 citations
It is concluded that originality, authorship, integrity, fairness, and transparency are interdependent concerns rather than separate issues, and that responsible AI use is best understood as a disciplined, disclosed collaboration with a non-author tool.
K. A. Badaru· Interdisciplinary Journal of...· 0 citations
This article argues student leadership programs can turn AI from a plagiarism risk into a laboratory for craft intelligence and deliberate stewardship, which safeguards how knowledge is created, tested, and shared.
Christine Haskell· New Directions for Student L...· 0 citations
The author reveals various aspects of responsible AI, including model interpretability, monitoring and management of model drift, and compliance recommendations, and practical insights into using AI governance tools to ensure fairness, bias mitigation, explainability, privacy compliance, and privacy in an enterprise se...
An AI-enabled lifecycle of Creation, Transformation, Transmission, Evaluation, Evaluation, and Governance is proposed and an AI-eWOM fit perspective is developed and a TCCM-organized research agenda identifies priorities for future research.
A. Joyal· Journal of business and mana...· 0 citations
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