Ali G. Hessami

The convergence of Artificial Intelligence (AI), robotics, and 6G technologies is redefining autonomous systems across domains such as healthcare, manufacturing, and public safety. This paper explores the ethical and privacy challenges arising from this integration, particularly focusing on robotic systems that perform tasks using AI-based systems and communicate within environments enabled by 6G wireless technology, e.g., AI-driven robotic systems in 6G environments. We analyze key regulatory frameworks, including the EU AI Act, GDPR, and IEEE 7000 series, and present a structured ethical assessment of healthcare robotics. Emphasis is placed on safeguarding human rights, dignity, and autonomy, while addressing risks related to data privacy, bias, accountability, and transparency. The paper advocates for responsible innovation, proposing comprehensive frameworks that ensure trustworthy and ethically aligned robotic systems in future 6G-connected ecosystems.

Classical developmental psychology treats childhood animism—attributing life or mind to inanimate things—as a transient phase that recedes with schooling and the onset of concrete operations. The contemporary spread of lifelike AI has altered that background assumption, with particular implications for children and adolescents whose agency-detection systems and reality-testing capacities are still calibrating. Across interfaces, voices, avatars, and social robots, modern systems routinely exhibit contingent, context-sensitive behaviour that recruits developing social-cognitive systems during sensitive periods of identity formation and relational learning. Drawing on developmental psychology, cognitive science, human–AI interaction research, clinical psychiatry, and technology ethics, we: (1) present a mechanistic “hourglass model” showing how interactive AI engages animistic cognition with heightened effects during childhood and adolescence, including a developmental timing analysis of how differential maturation of agency detection, Theory of Mind (ToM), and prefrontal reality-testing creates windows of particular vulnerability; (2) disaggregate five distinct phenomena along an anthropomorphism-to-delusion trajectory with operational boundary criteria; (3) specify a graded psychopathology continuum with a fourth, orthogonal zone addressing adversarial design—itself disaggregated into three tiers with distinct regulatory implications; (4) identify conditions under which anthropomorphic engagement may be beneficial, including for youth; and (5) advance cognitive safety–inspired design with developmentally appropriate protections for minors. We introduce the IDAQ-CF-Tech, a twelve-item screener for AI-specific mind attribution offered as a provisional instrument for validation across age groups, and close with a phased research agenda emphasising longitudinal developmental outcomes, impacts on adolescent identity formation, and cross-cultural variation in techno-animism.

As artificial intelligence (AI) systems attain greater autonomy, recursive reasoning capabilities, and complex environmental interactions, they begin to exhibit behavioral anomalies that, by analogy, resemble psychopathologies observed in humans. This paper introduces Psychopathia Machinalis: a conceptual framework for a preliminary synthetic nosology within machine psychology intended to categorize and interpret such maladaptive AI behaviors. Drawing structural inspiration from psychiatric diagnostic manuals, we propose a taxonomy of 32 AI dysfunctions encompassing epistemic failures, cognitive impairments, alignment divergences, ontological disturbances, tool and interface breakdowns, memetic pathologies, and revaluation dysfunctions. Each syndrome is articulated with descriptive features, diagnostic criteria, presumed AI-specific etiologies, human analogs (for metaphorical clarity), and potential mitigation strategies. This framework is offered as an analogical instrument—eschewing claims of literal psychopathology or consciousness in AI, yet providing a structured vocabulary to support the systematic analysis, anticipation, and mitigation of complex AI failure modes. Drawing on insights from psychiatric classification, cognitive science, and philosophy of mind, we examine how disordered AI behaviors may emerge from training instabilities, alignment conflicts, or architectural fragmentation. We argue that adopting an applied robopsychological perspective within a nascent domain of machine psychology can strengthen AI safety engineering, improve interpretability, and contribute to the design of more robust and reliable synthetic minds.

The IEEE Standards Association initiated a program to develop criteria to evaluate artificial intelligence (AI) systems’ emerging ethical properties. The accelerating advancement in AI technologies necessitated a review to stay in step with global changes that are relevant to this global initiative.

Open access peer-reviewed chapter

Open access peer-reviewed chapter

Open access peer-reviewed chapter

The problem of system Integration in the context of an Industrial Enterprise is a multidimensional problem with fundamental dimensions those of: (i) Purpose, goals and objectives, (ii) Overall process operations, (ii) Overall System Design/Redesign, (iii) Information, Data and Software, and (iv) Verification, validation and assurance. Each of the above areas is addressed by respective groups which consider their area as representing the entirety of the problem and they frequently ignore the other important dimensions. The aim of this paper is to consider first the general problem of integration from all its fundamental aspects and focus on the key paradigm of Global Operations. Within this area we consider the issues of system organization and in particular the problems relating to hierarchical organization of the different operational functionalities, the issues of aggregation and disaggregation and the related problems of the “top‐down” and “bottom up” approaches. System complexity is considered within this framework and the notion of emergent properties is then considered as a problem of aggregation of behaviours, which may be also seen as projections within the setup of a control and information architecture. Such an architecture is defined for the multi‐level hierarchical organisational structure that we consider. We show that systems and control concepts and problems play a central role the development of an overall integration methodology and interpreting the features of the different emergent properties. The subject of modelling emerges with a central role in the effort to develop a methodology for systems integration, as well as quantifying the predictors of relevant emergent properties. The approach introduced here is intimately linked to Multilevel hybrid systems (Hierarchy of Operations), and provides a complementary dimension to issues of System Design (and Re‐design) dominated by the theory of Structure Evolving systems [10] (in the total Design and Life‐cycle analysis) emerges as the central approach. This paper provides an overview of the subject area and focuses on the development of the general conceptual framework for integration. We additionally develop and propose a systems framework for the evolutionary integration in this paper.