AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
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.
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.
It is suggested that cross-domain differences are smaller than often assumed once stakes are controlled, and that stakes matter more than domain for understanding perceived risk when people evaluate AIES advice.
The importance of trust in artificial intelligence (AI) continues to grow, as trust is widely regarded as a critical prerequisite for organizational AI adoption. In this context, intention to use AI can be understood as a consequence of the decision to trust AI and is therefore strongly influenced by trust. Moreover, trust is regarded as essential for understanding the impact of increasing interaction with AI systems on both individuals and society. Much of the discussion on trust in AI relies on frameworks derived from trust in automation, but these approaches remain largely theoretical and insufficiently validated. One important empirical contribution addressing this gap is the path model developed by Karg, Ritz and Asprion (2025), which examined trust in ChatGPT using a student sample. This model conceptualizes perceived trustworthiness through performance, process, and purpose. Together with a user’s propensity to trust, these factors are assumed to determine trust in AI. Karg, Ritz and Asprion (2025) demonstrated that perceived trustworthiness is significantly shaped by users’ inherent propensity to trust, in turn, influences the intention to use AI. The present study replicates this path model using a business sample to assess the robustness of the original findings and to advance theory building. An online survey was conducted among 97 employees of a major Swiss bank, employing identical items and methodologies as in the original study. The replication largely supports the original findings. However, in contrast to the original study, performance did not significantly predict trust in AI in the business sample. The findings further reinforce the argument that users’ dispositional characteristics may play a more decisive role in shaping perceived trustworthiness of and trust in AI systems.
Agentic artificial intelligence (AI) is transforming social and organisational life by moving AI from an assistive tool into a semi-autonomous actor that can plan, recommend, communicate, coordinate workflows and trigger decisions. Existing debates on trustworthy AI often emphasise technical reliability, legal compliance or ethical principles, but social adoption depends on a subtler question: how do people learn when to trust, distrust, verify or refuse AI outputs in everyday work? This conceptual research develops the Social Trust Calibration Framework (STCF) for responsible human-AI adoption in organisations and public institutions. Using an integrative conceptual synthesis of trust theory, automation research, organisational sensemaking, algorithmic management, human-centred AI and contemporary AI governance guidance, the study identifies the mechanisms through which AI trust becomes either calibrated, excessive, deficient or displaced. The resulting framework argues that trust in agentic AI must be calibrated across four mutually dependent layers: capability trust, process trust, institutional trust and identity trust. The paper contributes a formal definition of calibrated AI trust, a social calibration loop, a diagnostic matrix of trust states, a maturity model and seven research propositions for future empirical testing. The analysis shows that appropriate reliance cannot be achieved by accuracy alone. It requires visible evidence, role-specific verification routines, accountable decision rights, psychological safety, transparent escalation paths and protection of human agency. The paper concludes that responsible AI adoption should be treated as a social trust calibration problem rather than a simple technology acceptance problem. This reframing offers practical guidance for leaders, educators and policymakers seeking to scale AI while preserving human judgement, legitimacy and social confidence.
It is argued that cognitive misalignment represents a likely impediment to AI adoption in many envisioned applications, and that addressing it is important for creating AI systems on which users are both willing and justified to rely.
The fast pace of instantiating the Artificial Intelligence (AI) in digital services has changed the manner in which organisations provide personalised, effective, and data-driven solutions in various fields like e-commerce, healthcare, and education. Even after these improvements, AI implementation by users is still not consistent, mainly because of the issues surrounding the areas of transparency, fairness, accuracy, and control. This lack of transparency, which is commonly called the black-box problem with many AI systems, has decreased the trust level and disposition of their users. To address this, Explainable Artificial Intelligence (XAI) has been proposed as a highly important concept to increase the level of transparency and user comprehension in AI-driven decisions. This research will explore the impact of the main XAI characteristics, such as the transparency of the algorithm, the perceived fairness, the perceived accuracy, and the perceived control on the perceived value of the users and, consequently, on their willingness to use AI-driven services. The paper relies on the Technology Acceptance Model (TAM) and the Theory of Consumption Values that suggested a combined model where perceptions of value serve as a mediating variable between the features of the AI systems and their intention to use them. The quantitative research design was used, and the primary data were gathered through the use of a structured questionnaire and a sample of 200 respondents in the National Capital Region (NCR) of India. The research adds to current literature, combining XAI characteristics with value-based and technology acceptance models, thus providing a more in-depth insight into the AI adoption in the new markets. Managerially, the findings demonstrate the need to create AI systems that are accurate and transparent, as well as fair and user-focused, to increase the perception of value and instigate user acceptance. On balance, it is possible to note that the study highlights the critical importance of the perceived value as a key process that connects the explainable features of AI to user adoption intentions.
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