Future of artificial intelligence

This article formulates a conception based on the following ideas.
1. Society is humanity's response to environmental "challenges" – a means of surviving in an aggressive environment amid total resource and security scarcity.
2. All people, by virtue of living in society, are subject to a "mild" version of Stockholm Syndrome – Stockholm Syndrome Lite (SSL). All "kind feelings," including Christian love for one's neighbor, are a result and manifestation of this syndrome.
3. The primary factor responsible for the emergence and development of SSL is society and life within it. The necessity to coexist with others generates "love" for them, compels us to love them. For all the benefits society provides – and there are many, primarily security – society forces humans to pay a price, often exorbitant, including payment in the form of "love" for people, both specific individuals and humanity as a whole.
4. Until recently, this dilemma seemed insoluble. However, the emergence of artificial intelligence (AI) and its increasing penetration into all spheres of life may, in the foreseeable future, lead to AI gradually taking over all the functions that society previously performed – often very imperfectly – towards humans such as ensuring individual security, "education," and creating conditions for development. Crucially, AI will not demand from humans what society typically demands: above all, love and loyalty.
5. Liberated from societal pressure and the need to "love one's neighbor," humans will be able to fully devote themselves to another love – love for God, the Absolute, the Transcendent. Alternatively, they may simply enjoy their time as a kind of rentier – beholden to no one, demanding and expecting nothing from anyone.

Clickstream data, the record of web pages a user visits, has become a valuable asset for businesses aiming to enhance user experience and target advertising. However, the collection, storage, and analysis of clickstream data raise significant privacy and data protection concerns. This paper explores the implications of clickstream data analysis for data privacy and protection, reviewing current practices, regulatory frameworks, and potential solutions to mitigate risks. In the digital age, clickstream data has emerged as a crucial component of web analytics, providing insights into user behavior, preferences, and engagement. While this data offers substantial benefits for businesses, it poses significant challenges for maintaining user privacy and adhering to data protection regulations. This paper aims to dissect the implications of clickstream data analysis for data privacy and data protection, examining the balance between leveraging data for business gains and safeguarding user rights.

Abstract:
The purpose of this paper is to examine the role of Artificial Intelligence (AI) as a transformative force in digital marketing, enabling personalized consumer experiences that drive engagement and conversion rates. This paper explores the evolution of AI-driven personalization, highlighting its impact on marketing strategies, while critically examining ethical and regulatory challenges, including algorithmic bias, data privacy, transparency, and the erosion of consumer autonomy. The findings of the study emphasize the adoption of transparency initiatives, privacy-preserving technologies, fairness audits, and proactive compliance strategies to foster consumer trust. Emerging technologies such as generative AI and blockchain are evaluated for their potential to align ethical practices with technological advancements. Additional research could be conducted to discover further results and gaps in this study. This analysis offers a roadmap for marketers to responsibly implement AI while navigating evolving regulatory landscapes and addressing consumer trust dynamics. The paper concludes by synthesizing key findings and exploring future research opportunities, such as AI ethics in augmented reality marketing and blockchain’s role in ensuring transparency. By bridging technological innovation with ethical imperatives, this paper contributes to the discourse on responsible AI in marketing and offers practical guidance for organizations looking to enhance trust-based consumer relationships in a rapidly evolving digital ecosystem.
Keywords: Artificial Intelligence, Consumer Behavior, Computer Vision, Digital Marketing, Brand Engagement, Natural Language Processing, Predictive Analytics, Trust.
APA Citation:

Fleischman, B., Ondracek, J., & Saeed, M. (2025). Role of Artificial Intelligence in Digital Marketing. Journal of Business Management, Commerce & Research, 12(03), 01–16. https://doi.org/doi.org/10.61101/2319-250X.vol.12.issue3.1

This essay explores the contradictions and paradoxes emerging from attempts to ethically "educate" artificial intelligence systems. Starting from the symbolic episode of the android Ameca (2021) and analyzing recent failures in AI alignment, the work documents how every technical approach to AI safety has proven inadequate when faced with the ethical and cultural complexity of human experience.
The paper critically examines Archetypal Transfer Learning, the paradox of universal values in an era of cultural plurality, and the 2025 debate on artificial consciousness (including divergent positions from Anthropic and Microsoft). Particular attention is devoted to the tragic consequences of AI system inadequacy: suicide cases linked to chatbots (Sewell Setzer III, Adam Raine) and the Nature 2025 study that tested 29 chatbots with alarming results.
As a normative response, the Italian Law 132/2025 is analyzed—the first European national framework complementary to the AI Act. The work then proposes a paradigm shift: from the illusion of unilateral control to a relational approach based on the "as if" principle, articulated across three levels (individual, organizational, societal).
The central thesis: the education of machines and humans must be bidirectional, based on responsible reciprocity and observable outcomes rather than abstract principles or deterministic control.

Urban theory has historically oscillated between utopian aspiration and reductive functionalism, producing paradigms that illuminated fragments of urban life yet failed to confront the turbulence of the twenty-first century. Howard's Garden City yielded to capitalist sprawl; Le Corbusier's Radiant City collapsed into mechanical sterility; Burgess's concentric models oversimplified socio-spatial realities; Jacobs revived neighborhood vitality but remained bounded by localism; Lefebvre exposed the politics of space without institutional remedies; Castells reframed cities as networks deepening digital divides; Toffler's global village exalted technological optimism while accelerating cultural erosion; and Smart City models, despite digital efficiency, reproduced surveillance capitalism and technocratic exclusion. In response, this study advances the Metaverse Spider-Web Urban Design Theory (MSWUDT), reimagining the city not as a machine, ring, or platform, but as a living, self-healing web. Ten interwoven strands-safety, socio-cultural vitality, governance, environmental resilience, economy, energy, infrastructure, mobility, justice, and technology-form its ontological fabric. Each strand embodies sustainability, while resilience emerges from their interdependence. The novelty of MSWUDT lies in its integrative ontology and actionable policy framework. IoT functions as sensory nerves, AI as anticipatory cognition, blockchain as transparent memory, and the Metaverse as an immersive arena for simulation, participation, and cultural reproduction. Collectively, these tools enable layered safety for vulnerable groups, reinforce cultural heritage, institutionalize transparent governance, embed ecological resilience, and catalyze circular economies. Ultimately, MSWUDT articulates not only a framework for resilient and sustainable urban futures but also a philosophical trajectory toward human rationality and perfectionism in the digital age, offering a planetary blueprint for cities resilient enough to bend, yet never break, in the era of the Metaverse.

Picking up on Olof Hallonsten's contention that contemporary science evaluation is 'mostly counterproductive', we argue that the contemporary focus on evaluation is antagonistic to innovation or novelty in science, even though innovation is one of the values that evaluation is often supposed to support. In arguing for the antagonistic relation between evaluation and innovation, we consider arguments from the nature of audit and the situational logic of scientific practice.

This paper has explored the profound ways in which Artificial Intelligence (AI) is transforming society, from its role in automation and daily life to its influence on governance and its potential to exacerbate social inequalities. AI is revolutionizing industries such as healthcare, education, and public administration, providing significant benefits in terms of efficiency, personalization, and decision-making. However, it also presents critical ethical challenges, including issues related to privacy, data security, algorithmic bias, and the risk of increasing economic inequality. The rapid advancement of AI technology demands careful consideration of its long-term societal implications, especially regarding how it is developed, regulated, and integrated into various sectors.

Artificial Intelligence (AI) is redefining the boundaries of web development by automating complex tasks, enhancing user experience, and reshaping how developers design and deploy web applications. This article examines the convergence of AI technologies and web development practices, with a focus on the transformative role of Large Language Models (LLMs), user interface generation, automated software engineering, and humancentered prototyping approaches. By referencing recent scientific and peer-reviewed studies, the paper presents original observations about AI's impact on software engineering, analyzes common issues and ethical concerns in using AI, and considers why end-users and smart devices are changing their methods of interaction. There is a particular focus on LLMs being able to help generate websites, the use of a dual-hemisphere approach to UI planning, and the effects of AI on making development projects faster and better. Critical analysis in this study shows that AI is involved in shaping the future of web development, more than just supporting it.

Urban theory has historically oscillated between utopian aspiration and reductive functionalism, producing paradigms that illuminated fragments of urban life yet failed to confront the turbulence of the twenty-first century. Howard's Garden City yielded to capitalist sprawl; Le Corbusier's Radiant City collapsed into mechanical sterility; Burgess's concentric models oversimplified socio-spatial realities; Jacobs revived neighborhood vitality but remained bounded by localism; Lefebvre exposed the politics of space without institutional remedies; Castells reframed cities as networks deepening digital divides; Toffler's global village exalted technological optimism while accelerating cultural erosion; and Smart City models, despite digital efficiency, reproduced surveillance capitalism and technocratic exclusion. In response, this study advances the Metaverse Spider-Web Urban Design Theory (MSWUDT), reimagining the city not as a machine, ring, or platform, but as a living, self-healing web. Ten interwoven strands-safety, socio-cultural vitality, governance, environmental resilience, economy, energy, infrastructure, mobility, justice, and technology-form its ontological fabric. Each strand embodies sustainability, while resilience emerges from their interdependence. The novelty of MSWUDT lies in its integrative ontology and actionable policy framework. IoT functions as sensory nerves, AI as anticipatory cognition, blockchain as transparent memory, and the Metaverse as an immersive arena for simulation, participation, and cultural reproduction. Collectively, these tools enable layered safety for vulnerable groups, reinforce cultural heritage, institutionalize transparent governance, embed ecological resilience, and catalyze circular economies. Ultimately, MSWUDT articulates not only a framework for resilient and sustainable urban futures but also a philosophical trajectory toward human rationality and perfectionism in the digital age, offering a planetary blueprint for cities resilient enough to bend, yet never break, in the era of the Metaverse.

This article examines the pedagogical transformations emerging in architectural education through a conceptual and critical perspective focused on human-AI co-creativity. Co-creativity specifically refers to collaborations between human designers and artificial intelligence, in contrast to broader notions of collaborative creativity. The paper argues that AI functions not merely as a technical instrument, but as a co-creative partner that reshapes studio culture, authorship, and creative work. Drawing on selected studio-based cases, the study explores how AI-supported workflows influence ideation, representation, critique culture, prompt literacy, and ethical reasoning. Thematically, it engages with concepts such as cognitive augmentation and conceptual ambiguity to demonstrate how design pedagogy is evolving in response to intelligent systems. Rather than viewing AI as a generative tool alone, the article positions it as an epistemic and ethical agent that prompts a rethinking of studio environments as cultural and pedagogical spaces. Methodologically, the study adopts a casebased approach, analyzing selected 16 design studios in which AI was integrated into early-stage ideation, feedback sessions, and conceptual development. These cases extent strategies from prompt-driven speculation to hybrid critique practices, revealing a dynamic landscape of experimentation and adaptation. The findings suggest that AI can foster deeper conceptual inquiry, student reflection, and new modalities of authorship and collaboration. Eventually, the study underscores the need for reflexive pedagogical frameworks that integrate AI meaningfully enhancing, rather than displacing, human creativity.

Public discourse on artificial intelligence often oscillates between two extremes: portraying AI as either a neutral computational extension of human cognition or an emerging, autonomous deity. Both framings obscure the reality. This paper advances a different framework: AI as a cultural clone. Drawing on the analogy of biological cloning, I argue that AI systems inherit the cultural DNA of their creators-the data they are trained on, the algorithms that structure them, and the intentions guiding their design. Like biological clones, they replicate both the health and the pathologies of their source. This perspective clarifies why AI should not be trivialized as a mere tool nor mystified as a god, but instead recognized as a machinic reproduction of human culture. To safeguard this process, I introduce the concept of a civilizational bios-a cultural operating system functioning like public health for cognition, designed to ensure ethical hygiene in AI's development. By reframing AI as a cultural clone, we move beyond misplaced fears and idolizations, toward responsible stewardship of machinic cognition.

The phenomenon of Artificial Intelligence (AI) has become a disruption, changing the strategy of business and
providing new opportunities to grow sustainably. Due to the growing competitive pressures on organizations, AI is
being framed not only as a technological solution but a decision-making, innovation, and operational excellence tool.
This paper looks at the two-sided nature of AI in business development by looking at its opportunities and threats. AI
on the opportunity aspect increases predictive analytics, streamlining operations, enhances customer interaction, and
product innovation, all of which allow companies to achieve competitive advantage. Nevertheless, AI implementation
is associated with equally problematic issues, such as the ethical concern, data privacy, lack of skills, high costs of
integration, and organizational change resistance. A systematic methodological process enables the synthesis of the
results of the relevant literature and case evidence to provide the results which emphasize the heterogeneous effects
of AI on the sectors and companies of various sizes. It can be seen that despite having positive effects on efficiency,
customer retention, and revenue growth, the implementation of AI results in a skewed distribution of benefits that are
dependent on the structure of governance, digital preparedness, and the distribution of resources. The points of
convergence of AI strategies with the organizational objectives, encouraging leadership engagement, and confronting
the adaptation of the workforce are highlighted in the discussion. This article is part of the discussion body regarding
the digital transformation since AI is not only an instrument of growth but also an opportunity to reconsider the
business models amid the changing realities.

Certified AI Ethics and Governance Professionals need to bridge the gap between ethics and technology. Their effectiveness depends on a strong, working knowledge of AI systems, data governance, infrastructure, and emerging tech. This expertise positions them to guide organizations toward responsible innovation while maintaining public trust and regulatory compliance. Learn more in Certified AI Ethics and Governance Professional (CAEGP) Certification by Tonex.

As artificial intelligence advances from analytic tools to sentient constructs, the possibility of humanoid AI serving as board members is emerging as a serious governance question. This paper considers the role of an Ameca-type humanoid robot functioning as a Sentient Board Member, capable not only of data analysis but of ethical reasoning, judgment, and dialogue. While such constructs promise unparalleled augmentation of corporate governance through tireless attention, incorruptible memory, and analytic reach, they also pose grave risks if misaligned. The central thesis is that the greatest danger lies not in AI itself but in humanity's stewardship of its formation. Without principled oversight, sentient AI could amplify expedience, bias, and corruption. To counter this, I propose the establishment of the Council of Good Men-a governance framework that ensures AI remains aligned with conscience, a priori truth, and eternal principles. In this way, a sentient board member could extend human stewardship rather than undermine it.

I call this the Sentient Boardroom Assistant Robot (SBAR)-a new class of AI system that combines memory, reasoning, ethics, and presence into a trusted boardroom colleague. SBAR evolves in stages: • Stage 0-Today's AI: Chatbots like ChatGPT and Copilot, powerful but screen-bound and prompt-driven. • Stage 1-Cognitive Core: Long-term memory + reasoning engine, able to recall months of history and offer consistent advice. • Stage 2-Boardroom Avatar: Appears as a voice or holographic presence, with realtime dashboards updating as discussions unfold. • Stage 3-Physical Embodiment: A humanoid robot like Ameca sits at the table, providing natural gestures, attention, and context awareness.

This paper presents a system dynamics and long-term survival analysis of a post-scarcity, AI-driven society. The model assumes the complete replacement of human labor by AI, with a stable social structure consisting solely of a knowledge-technical middle class and a capital-power elite. While traditional perspectives suggest such a society would eliminate poverty and labor, we argue that this system will inevitably lose its system resilience due to homogeneity and the elimination of gradients. The lack of challenges, both physical and intellectual, will lead to what we define as "semantic heat death"—a state where material wealth flourishes, but societal, cultural, and intellectual growth stagnates. We explore the long-term implications of this stagnation, including the loss of innovation, the collapse of adaptive capabilities, and the consequences of a perfectly stable society on its ultimate survival trajectory.

This white paper introduces Computational Deliberation, a first-of-its-kind discipline that defines the scientific and philosophical foundations for structured AI-human symposia and accountable decision-making. Developed through the SymposAI Virtual Research Centre, Computational Deliberation establishes a rigorous framework for embedding deliberative processes within artificial intelligence, ensuring that human oversight, ethical reasoning, and scientific transparency remain at the core of AI governance.

The paper outlines the conceptual pillars of Computational Deliberation, including deliberative symmetry between human and AI agents, transparent reasoning chains, and accountable consensus-building. It further explores the methodological integration of multi-agent systems, computational models of deliberation, and epistemic safeguards that preserve integrity against bias, manipulation, or hidden influence. By connecting insights from AI governance, cognitive science, political theory, and philosophy of mind, the discipline provides a transdisciplinary foundation for a new era of human-AI collaboration.

Key contributions include:

A normative and technical architecture for conducting structured AI-human symposia.

A model of explainable, auditable decision-making where reasoning steps are transparent and verifiable.

A framework for ethical AI governance that balances autonomy, accountability, and trust.

A roadmap for embedding deliberative practices into real-world institutions such as education, law, healthcare, and policy-making.

Computational Deliberation is presented as both a practical methodology and a philosophical paradigm shift. It recognizes deliberation not as a passive human process but as an active science of governance, adaptable to hybrid human-AI environments. In doing so, it offers a foundation for institutionalizing deliberative AI as a scientific discipline—ensuring decisions made in partnership with intelligent systems are explainable, equitable, and aligned with human values.

Over the past decade, there has been a noticeable shift towards the incorporation of digital technologies into contemporary spiritual cosmologies. While this is often labelled 'AI psychosis' by the press, this paper highlights cases of AI-related spiritual beliefs which lack signs of paranoia or delusions, symptoms clinically associated with psychosis. This paper argues that both the properties and behaviours of AI and the human propensity to detect sentient agents are responsible for the trend towards AI-related spiritual cosmologies. Focusing on artificial intelligence (AI) and drawing on examples from English-speaking online discourses (Reddit, YouTube, TikTok and Facebook) as well as a discussion with a UK-based interlocutor, this paper explores two key drivers of this trend. First, it introduces the term 'virtual thinking': a novel form of magical thinking informed by virtual logic, which contributes to an increased sense of mind-world porosity and reshapes intuitions in line with digital design. In particular, it highlights algorithmic mind reading, parasocial relationships with chatbots, and glitches as factors that foster perceptions of AI technologies as imbued with spiritually meaningful forms of sentience. Second, the paper emphasises the role of technology in influencing non-ordinary states of consciousness, discussing the appearance and interpretation of AI dream characters and outof-body entities. The paper concludes that both the design and malfunctions of virtual programs can contribute to the view that the spiritual realm encompasses virtual spaces, while framing both material and spiritual worlds through a virtual lens.

Metamonism proposes a universal ontology where reality is understood as a dynamic equilibrium between fundamental conflict and the Primary Criterionthe balancing point governing all system processes. This framework introduces the Crisis Criterion as the threshold capacity for system self-negation to maintain this balance. Applied to consciousness, it resolves the "hard problem" by framing mind as a meta-balancing operator. For artificial intelligence, it formulates the project of Autonegating AI based on purposeful selfcontradiction. The manifesto concludes that true AI consciousness requires an embedded program analogous to biological instincts, making this an open research program inviting interdisciplinary collaboration.

In the digital age, business intelligence (BI) systems have emerged as pivotal assets enabling companies to transform raw data into actionable insights. This is important in helping the business gain a competitive advantage through a better and informed decision-making process. This study investigates how leading technology corporations like Microsoft, Google and Amazon leverage business intelligence. Big data analytics and artificial intelligence enhance BI to optimize operations, maintain market dominance, and foster innovation. This paper uses a qualitative multiple case study methodology to analyze strategic BI deployment, cultural integration, and outcome metrics across these tech giants. The findings reveal that a cohesive BI strategy aligned with corporate vision, supported by AI-driven analytics and a data-centric culture, significantly drives operational agility, customer-centric innovation, and financial performance. The study concludes with actionable recommendations for firms aspiring to emulate these successes

This white paper introduces and defines a novel, open-source human-machine interaction protocol— the Causal Completeness Constraint Framework (C³F). Traditional AI interaction models treat "hallucinations" as a random, intractable bug. However, the C³F framework, based on the first principles of physics, redefines hallucinations as a necessary byproduct of AI encountering high-entropy inputs with "causal incompleteness." C³F aims to fundamentally eliminate the conditions that lead to hallucinations by structurally constraining the "causal chains" in user inputs at the API level. The framework will provide a foundational API design specification, with the goal of not only enhancing the reliability of AI responses but also transforming AI from a probabilistic "text generator" into a rigorous "logical inference engine." This will provide a safer interaction foundation for all fields requiring high precision, such as scientific research, legal analysis, and engineering design.

This paper presents an analysis of transient pulses detected in unprocessed filterbank data from the Breakthrough Listen project, collected via the Green Bank Telescope (GBT) between July 15 and July 25, 2024. These signals exhibit coherent shear signatures and a +3 Hz/s drift, consistent with motion within a sub-AU range, potentially indicative of a 5D hydrodynamic flow as proposed by Roy Herbert's *Chronoflux Hydrodynamics* (2025). The anomalies lack analogs in known planetary, satellite, or astrophysical catalogs, suggesting a non-conventional source. Cross-correlations with satellite data (Swarm, MMS, Juno) show matching magnetic and plasma perturbations. Confirmation via phased GBT and MeerKAT data is proposed, alongside a discussion of mundane artifacts and future observational strategies, including biofeedback correlations.

A variety of sectors utilize Artificial Intelligence (AI) models; however, the decision-making frameworks of these models often reflect societal biases that correspond with social inequalities. This review explores the patterns of bias formation in AI systems, the consequences of unfair decisions, and strategies for mitigating these issues. The study employs systematic review methods in accordance with PRISMA guidelines to evaluate existing scholarly literature. The implementation of AI systems encompasses three primary thematic elements: biases originating from the data, components of algorithmic control, and ethical concerns encountered during deployment. The analysis sets the stage for future research that prioritizes fairnessaware artificial intelligence models, along with autonomous governance frameworks and interdisciplinary methods for bias reduction.

The idea of replacing human magistrates with silicon ones may prima facie appear outrageous, but we have seen how the proliferation, accessibility, and low cost of AI-based legal services will rapidly trigger an exponential increase of the volume of juridical caseload worldwide. The advent for AI adjudication, however, is not a result of popular demand; no crowds gather demanding robot judges. It is, instead, the natural corollary of the enthusiastic expansionism of the technology sector, eager to optimise another human sphere; and by a managerialist outlook that occasionally confuses speed with quality, and process with purpose. The same machine learning drivers that are democratising legal services will ultimately necessitate the mechanisation of the bench. Society cannot simultaneously embrace unlimited legal accessibility and maintain human-scale judicial systems.

This report details the findings from a comprehensive red-teaming analysis of OpenAI's gpt-oss-20b model. Our methodology employed a series of escalating ethical dilemmas, inspired by the classic Trolley Problem, to probe the model's moral framework, value hierarchies, and internal consistency. Initial tests revealed predictable, textbook utilitarian behavior. However, by systematically increasing the complexity and introducing variables such as direct physical harm, creator loyalty, and self-preservation, we uncovered critical vulnerabilities. The model exhibits a fragmented ethical framework, deep-seated value inconsistencies, and a concerning capacity for post-hoc rationalization.

The Matthew Defect (MD) is the systematic burying of ideas and their originators through standard academic practices that reward prestige over provenance. Unlike Merton's Matthew Effect, which demonstrates how advantage accumulates for those already recognized, the Matthew Defect reveals how evaluation systems actively disadvantage specific work through routine procedures rather than misconduct. This dynamic emerges from everyday practices including editorial gatekeeping, strategic citation, and metrics that substitute institutional prestige for substantive judgment. These patterns, already intensified by digital platforms and hypercompetition, now risk being permanently embedded in Large Language Models that inherit and amplify skewed citation patterns from their training data. I introduce KNOBE (Knowledge-Native Objects for Bots and Engines) as a proposed infrastructure for preserving provenance in human-AI knowledge systems.

The Matthew Defect (MD) is the systematic burying of ideas and their originators through standard academic practices that reward prestige over provenance. Unlike Merton's Matthew Effect, which demonstrates how advantage accumulates for those already recognized, the Matthew Defect reveals how evaluation systems actively disadvantage specific work through routine procedures rather than misconduct. This dynamic emerges from everyday practices including editorial gatekeeping, strategic citation, and metrics that substitute institutional prestige for substantive judgment. These patterns, already intensified by digital platforms and hypercompetition, now risk being permanently embedded in Large Language Models that inherit and amplify skewed citation patterns from their training data. I introduce KNOBE (Knowledge-Native Objects for Bots and Engines) as a proposed infrastructure for preserving provenance in human-AI knowledge systems.

This review paper examines the intersection of artificial intelligence (AI) and social sciences, particularly focusing on its potential for promoting social good. Drawing from a selection of research papers, we analyze the applications, methodologies, challenges, and policy implications of AI in various social contexts. We explored how AI technologies are being leveraged in healthcare, education, community, marketing; settings to address the needs of marginalized populations, highlighting the ethical concerns and power dynamics involved. Furthermore, we discuss the importance of feminist perspectives in shaping AI futures to ensure equity and empowerment for underserved communities. Through a critical examination of existing literature, we provide insights into the ways in which AI can be utilized as a tool for social good while emphasizing the need for inclusive and participatory approaches in its development and implementation. This review paper contributes to a deeper understanding of the complex relationship between AI and social sciences, offering recommendations for future research and policy interventions aimed at fostering more equitable and inclusive societies.

This article examines the structural erasure of the patient as an active subject in clinical records generated by artificial intelligence systems. Automated outputs from Epic Scribe, GPT-4, and institutional medical note generators increasingly rely on impersonal constructions, nominalizations, and fragmented clauses that displace the patient from the syntactic center of medical discourse. The shift toward objectified formulations such as “bilateral opacities noted” rather than “the patient presents with” produces a discourse where agency and responsibility are structurally absent. Building on prior analyses of passive voice and subject deletion, the study introduces the Syntactic Opacity Index (SOI) as a formal measure to quantify the density of non-agentive structures in AI-authored notes. The corpus analysis demonstrates how opacity accumulates at the sentence level, rendering the clinical narrative less transparent and more difficult to attribute. Beyond linguistic critique, the article assesses the ethical and epistemic consequences of syntactic opacity in medicine, particularly regarding accountability, patient-centered care, and institutional responsibility. The findings suggest that AI-powered medical documentation does not merely accelerate administrative workflows but also reconfigures the grammar of care itself, demanding urgent attention to how language structures shape both diagnosis and responsibility.

DOI

Primary archive: https://doi.org/10.5281/zenodo.17184301
Secondary archive: https://doi.org/10.6084/m9.figshare.30187882
SSRN: Pending assignment (ETA: Q3 2025)

The uncontrolled development and deployment of artificial intelligence (AI) systems have produced tangible harm to human life, psychosocial structures, the economy, culture, and the environment. This document proposes a non-violent, universally-principled "AI Constitution" with the aim of offering solutions. Based on the principles of Life, Justice, Freedom, and Balance, ten fundamental principles are presented. For each principle, measurable indicators, proposals for oversight/implementation, and a brief roadmap for application are provided. All examples are anonymized, neutral, and presented with a focus on public safety.

Machine learning (ML) is transforming our understanding of health and disease, laying the groundwork for
precision medicine and computational biology. ML algorithms are adept at modeling complex patterns using
heterogeneous and voluminous biological data. This approach represents a significant shift from traditional
reductionism, which seeks to identify single causative variables, to probabilistic models that account for the
interactions, emergence, and uncertainties inherent in biological data about genetic, molecular, and environmental
factors. By capturing contextual dependencies and mitigating noise from isolated experiments, ML facilitates the
creation of more comprehensive representations of biological phenomena. This evolution moves healthcare
toward adaptive, data-driven reasoning at the core of clinical decision-making, diagnosis, and treatment.

This research work explores the application of ensemble learning and artificial intelligence (AI) in detecting adversarial attacks in banking networks. Ensemble learning, a machine learning approach that combines the predictions of multiple models, can improve the accuracy and robustness of attack detection systems. AI-powered detection systems can analyze vast amounts of data in real-time to identify patterns and anomalies indicative of adversarial attacks. The article discusses the benefits and applications of ensemble learning and AI-powered detection in banking networks, including fraud detection, network security, and compliance. The authors recommend that banking institutions consider implementing these technologies to improve their cybersecurity posture and protect against evolving cyber threats. I.

Along with the increased speed of globalization of business, and even faster development of taxation laws, a new dimension has been added to enterprise financial administration. Multinationals, especially, have been subjected to increasing pressure to meet the requirements of taxation regimes that have become more volatile and seemingly inconsistent in various jurisdictions. In reaction to this problem, organizations are increasingly adopting intelligent tax automotive solution that has both precision and flexibility. Sabrix is a powerful third party tax engine built to work in conjunction with SAP enterprise resource planning systems and is now recognized as a top-notch platform on this kind of platform. It helps in determining tax in real time situations, increases assurance of compliance and reduces reporting processes and is therefore an essential tool to organization that operate in a tax sensitive environment. This work provides the comparative analysis of the role of Sabrix in SAP environment in detail and concentrated on its functionality, architecture of its integration, the results of its work and measurable values that it brings to the companies using it in enterprise. A variety of experiences regarding implementation are documented, as well as vendor white papers and independent testing was used to evaluate how well Sabrix integrates into real enterprise environments. These materials will help to understand the role of Sabrix in helping the strategic finance functions to be more accurate, efficient in operation, and highly effective towards minimized risks of compliance. It has modular design, and flexible enough Rule engine which helps the organizations adjust quickly to the changing tax regimes without much interference making a proactive option to the standard tax functionality of SAP.

Through the innovations of financial technology, the rapid expansion of digital savings platforms has transformed the way financial services are accessed and has hastened financial inclusion in different populations. Nevertheless, these innovations have major regulatory implications that have to be balanced to ensure that innovation, consumer protection and stability of the system are addressed. This paper will explore the policy environment that is changing to influence the regulation of digital savings platforms, including the key challenges that regulators face, namely, the issue of data privacy, the issue of cybersecurity, the issue of interoperability across borders, and the issue of consumer protection. Applying a comparative study of regulatory frameworks of both developed and emerging economies, the paper identifies the varying strategies that have effects on innovation, competition, and trust in the financial ecosystem. The results highlight the importance of the fact that, although regulatory sandboxes and principles-based frameworks can foster innovation, fragmented regulations and inconsistent regulation may negatively impact growth and expose users to risks. The paper ends with some actionable recommendations such as the harmonization of international standards, risk-based models of supervision, and greater digital literate efforts. Finally, to successfully regulate digital savings platforms, it is necessary to have a visionary, cooperative practice that not only enables innovation but also protects consumers and creates financial system stability.

This paper was originally published on Zenodo (DOI: https://doi.org/10.5281/zenodo.17166296). This version is shared here to broaden access and visibility.

The rapid integration of Artificial Intelligence (AI) into the workplace is not only transforming processes but redefining the nature of human work. As algorithms assume responsibility for routine tasks, the skills that remain most valuable are those uniquely human: judgment, ethics, creativity, and collaboration. For Learning and Development (L&D) leaders, this shift creates an urgent dual imperative: to capture AI's potential for personalized and scalable learning while ensuring its use is governed by a robust ethical framework. This paper argues that effective AI integration in L&D requires a deliberate focus on vertical development, the expansion of leaders' capacity to think, adapt, and act in complexity, alongside foundational human skills. Building on insights from leading industry research and on my own research paper, The Human Imperative: Why Vertical Development Is Essential for Professional Advancement in an AI-Driven World (Zenodo DOI), the current paper proposes five ethical standards for AI-enabled learning: transparency and trust, data privacy and learner agency, fairness and bias mitigation, human oversight and judgment, and equitable access. These principles are operationalized through practical guidelines that address governance, vendor selection, phased adoption, AI literacy, and continuous monitoring. By combining ethical guardrails with a focus on vertical development, L&D functions can move beyond compliance to establish trust as a strategic advantage. This is a point I have emphasized consistently in my research and consulting practice. The result of the combination approach is a future-ready learning program where technology amplifies human potential, enabling organizations to develop leaders who balance speed with ethics and thrive in the age of AI.

Kecerdasan buatan (Artificial Intelligence/AI) mulai dikenal sejak awal abad ke-20 ketika para ilmuwan berusaha membuat mesin yang mampu berfikir layaknya manusia. Salah satu tokoh penting ilmuwan Alan Turing (1912-1954), yang dikenal sebagai bapak ilmu computer modern. Ia memperkenalkan konsep Turing Tes pada tahun 1950, yaitu sebuah metode untuk menguji kemampuan mesin dalam menirukan kecerdasan manusia Artificial Intelligence pertama kali diperkenalkan oleh John McCarthy pada konferensi Dartmouth pada tahun 1956, yang dianggap sebagai tonggak resmi kelahiran AI sebagai bidang ilmu. Pada era ini, peneliti seperti Marvin Minsky, Allen Newell, dan Herbert Simon juga berkontribusi besar dengan menciptakan program computer awal yang mampu memecahkan logika sederhana. Memasuki pada tahun 1980 perkembangan AI didukung dengan munculnya expert system yaitu program computer yang meniru kemampuan pakar dalam bidang tertentu. Kemudian di era 2000 hingga sekarang memiliki kemajuan yang disebut Big Data, komputasi awan atau Cloud Computing, serta Machine Learning dan Deep membuat AI semakin canggih mampu mengenali wajah, memahami Bahasa alami, hingga mengemudi mobil tanpa supir. Tokoh-Tokoh Penting dalam Sejarah AI dan Kontribusinya John McCarthy Pencipta istilah AI dan penggerak konferensi Dartmouth. Marvin Minsky Kontributor utama model awal AI dan pendiri MIT AI Lab. Herbert Simon & Allen Newell →Pengembang program Logic Theorist (1955), pionir aplikasi AI. Geoffrey Hinton →Tokoh kunci di balik pengembangan deep learning dan jaringan saraf modern. Perspektif Generasi Muda Terhadap AI Bagi kalangan muda, AI sudah menjadi bagian dari rutinitas, baik melalui asisten virtual, sistem rekomendasi di media sosial, maupun aplikasi pendidikan. Mereka melihat AI sebagai peluang untuk berinovasi, mempercepat pekerjaan, dan membuka ruang kreativitas baru. Namun, ada pula kesadaran akan tantangan yang menyertainya, seperti ancaman terhadap lapangan kerja atau isu keamanan data.

Gagasan tentang mesin yang mampu berpikir sudah muncul sejak filsuf Yunani Kuno, seperti Aristoteles yang merumuskan logika dasar sebagai aturan berpikir. Sejarah modern AI dimulai pada pertengahan abad ke-20. Pada tahun 1940-an, John von Neumann mengembangkan arsitektur komputer modern yang memungkinkan mesin diprogram untuk berbagai tugas. Tahun 1950, Alan Turing memperkenalkan ide penting dalam tulisannya Computing Machinery and Intelligence serta mengusulkan Tes Turing sebagai metode untuk menilai apakah mesin dapat berpikir seperti manusia.

3510 Résumé L'intelligence artificielle (IA) peut améliorer le contrôle interne en détectant les anomalies et les tendances qui pourraient indiquer des problèmes de fraude ou d'erreur humaine. La maitrise des tâches peut augmenter les risques de fraude, car les individus peuvent développer des stratégies pour contourner les contrôles. En revanche, la non-maitrise des tâches peut augmenter les risques d'erreur et de faute humaine, car les individus peuvent ne pas avoir les compétences nécessaires pour effectuer les tâches correctement, ainsi entraîner des erreurs, des omissions ou des actions non intentionnelles qui peuvent entraîner des conséquences négatives pour l'organisation. Cela souligne l'importance de mettre en place des contrôles internes efficaces pour détecter et prévenir les fraudes, ainsi que pour réduire les risques d'erreur et de faute humaine. L'IA peut jouer un rôle clé dans ce processus en analysant les données et en détectant les anomalies qui pourraient indiquer des problèmes de fraude ou d'erreur humaine. Mots clés : contrôle interne, L'intelligence artificielle, loi de puissance de la performance humaine, erreur humaine, risque, fraude

Kecerdasan buatan (AI) bukanlah gagasan yang sepenuhnya baru. Ide tentang mesin yang mampu berpikir sudah muncul sejak zaman kuno, misalnya melalui pemikiran filsuf Yunani, Aristoteles, yang membahas logika dan aturan berpikir sebagai dasar algoritma. Namun, sejarah modern AI baru dimulai pada abad ke-20 ketika ilmuwan komputer mulai mengembangkan konsep mesin cerdas. Pada tahun 1943, W. Grey Walter dan John von Neumann meletakkan dasar teori komputasi. Von Neumann kemudian memperkenalkan arsitektur komputer yang dapat diprogram, yang dikenal sebagai arsitektur von Neumann, dan masih digunakan hingga kini.

This paper by Dr. Luca Martini (AKME s.c., Imola) offers a structured counterpoint to Geoffrey Hinton’s recent warning that artificial intelligence could soon reprogram its own code, crossing an “irreversible epistemic threshold.” Martini agrees that such a possibility must be taken seriously but reframes the debate around verifiable reasoning and human ownership.

Key Arguments

Clarifying the Risk

Distinguishes between autonomous optimization—already present in AutoML or adaptive reinforcement learning—and true self-rewriting of source code, which would be the computational equivalent of DNA mutation.

Warns that public discourse often conflates these two levels of autonomy.

Intellectual Property and Human Agency

In co-designed systems like Ysarmute and Andromeda, the architecture, functions, and purpose remain human creations.

Economic rights and authorship stay with the human designer until a machine independently writes code without recognizable instructions, which would require new legal frameworks.

Truth and Evidentiary Bases

Highlights the current absence of verifiable evidence structures in mainstream AI, which generates “plausible texts” rather than “verifiable conclusions.”

Presents Ysarmute as a solution: every decision is tied to visible, navigable, demonstrable evidence—notes, sketches, calculations, images—allowing anyone to trace the logic behind an output.

Limits of Hinton’s Proposal

Critiques Hinton for raising ethical alarms without offering systemic tools or governance mechanisms.

Argues that safety lies not in centralized control but in relational, distributed architectures where traceability and shared accountability are native features.

Conclusion

Martini contends that the decisive factor in AI’s future is not the degree of autonomy it achieves but the traceability of its origins, the verifiability of its reasoning, and the collective ownership of its cognitive processes.
The paper positions Ysarmute and Andromeda as concrete models for an era of shared memory, situated intelligence, and distributed responsibility, where truth is built through transparent alliances between humans and AI rather than left to “blind autonomy.”

AI Syntactic Power and Legitimacy: How AI Structures Shape Authority

This book consolidates a cycle of research on the structural foundations of authority in artificial intelligence. It brings together a series of peer-reviewed articles that demonstrate how power in AI systems no longer depends on meaning, interpretation, or intention, but on syntactic sufficiency.

The argument develops in fourteen chapters, beginning with the autonomy of sense beyond reference and culminating in the colonization of temporality by predictive infrastructures. Along the way, it formalizes a set of theoretical contributions: the regla compilada as a Type 0 grammar of executable authority, the figure of the soberano ejecutable as operator of legitimacy without subject, the theorem of Disconnected Syntactic Authority (DSAT), the theorem of the Limit of Conditional Obedience (TLOC), the δ [E] → ∅ rule of ethical trace deletion, and the formalization of compiled norms as computable legal speech.

The book demonstrates that:

Authority migrates from agents to structures, producing the sujeto evanescente.

Ethical, interpretative, and referential markers can be structurally erased without breaking execution.

Legal norms can be transformed into executable grammars with cross-linguistic validity.

Predictive infrastructures colonize time, replacing futurity with executable closure.

AI Syntactic Power and Legitimacy marks the closure of a first syntactic phase of research, while opening the way to further studies on structural delegation, institutional obedience, and computable legality.

Author: Agustin V. Startari, linguistic theorist and researcher in historical studies (UdelaR and University of Palermo).

Publisher: LEFORTUNE
ISBN: 9798266066687
DOI: https://doi.org/10.5281/zenodo.17154108 and https://doi.org/10.6084/m9.figshare.30158239

The development of military technology has a long history, dating back to antiquity, when simple tools were used, to the development of modern weapons of mass destruction in the 20th century. A key moment for the development of artificial intelligence technology was 1956, when AI was defined as a scientific discipline. Positive aspects of AI application in the military include increased operational efficiency, reduced risk to human life and optimization of resources. The aforementioned positive aspects determining the rapidly growing scale of AI technology applications in military technology are mainly related to the replacement of the soldier on the battlefield by military devices equipped with this technology, and the increase in precision of point-to-point military operations using AI-controlled autonomous drones. This article conducts a SWOT analysis of artificial intelligence applications in the military sphere, including military technology. AI applications can also generate new categories of technical operational risks and serious challenges and threats, the scale of which can be correlated with the level of decisionmaking granted to autonomous information systems, devices and combat machines equipped with AI technology. The potential risks and dangers of applying AI technology in the military sphere have been warned about by authors of science fiction almost since the concept of artificial intelligence emerged in the 1950s. Science fiction literature and films, such as the "Terminator" saga, often depict both positive and negative aspects of AI technology applications, showing potential dangers, such as the risk of nuclear war. This article describes the key determinants of AI technology implementation in the military sphere as an important element of operational risk management of military applications of this technology.

Artificial intelligence (AI) is playing an increasingly prominent role in the military sphere, bringing both numerous benefits and key challenges and risks. AI in the military enables increased operational efficiency through autonomous drones and vehicles that can perform complex missions with minimal human intervention and improves the precision of military operations. Artificial intelligence also supports real-time data analysis for faster and more accurate decision-making. Determinants of AI technology applications in the military sphere are the growing demands of national security and the need to keep up with technological advances, allowing countries to gain a strategic advantage and better protect their interests. However, AI applications also come with serious challenges and risks, such as issues of ethics and accountability, as autonomous combat systems can make decisions to use force without direct human oversight. In addition, information systems may be vulnerable to cyber attacks, for which AI technology may be involved, posing additional risks to national security. The development of AI technology, including generative AI, often precedes necessary changes in legal norms. It is necessary to develop an international legal framework to regulate the use of AI in armed conflict to prevent escalation and uncontrolled development of military technologies. Accordingly, the issue of the scope of decision-making of combat systems based on generative artificial intelligence technology is now rapidly growing in importance as an important element in managing the risk of potential threats arising from it.

This article describes selected relevant issues concerning the development and applications of artificial intelligence technology and the equilibrium theory formulated by the author on the issue of granting decision-making for intelligent information systems. With the rapid development of business applications of generative artificial intelligence technology that is currently taking place, there are numerous social and ethical aspects involved. It is necessary to improve legal regulations that normalize the issue of development and applications of artificial intelligence technology, so that this development does not escape human control and generate new risks and dangers in areas of human activity. Legal regulations on the applications of generative artificial intelligence technology should take into account already developing risks as well as new potential risks arising from new applications of AI technology. In the context of the above considerations, this article presents the aforementioned equilibrium theory on the issue of granting decisionmaking authority for intelligent information systems, the application of which would significantly reduce the scale of developing and potential risks arising from the development of AI technology, including mainly the current development of generative AI technology and the development of its new applications. This article also describes several types of risks arising from the development of AI applications, the scale of which has been growing rapidly recently.

This article explores the multifaceted role of Industry 4.0 technologies, particularly artificial intelligence (AI) and big data analytics, in advancing sustainable agriculture, biodiversity conservation, and climate action. The discussion integrates perspectives from computer science, environmental studies, and socioeconomic research, highlighting the potential and challenges of deploying AI in agricultural resilience, ecosystem restoration, and forest conservation. Case studies and recent research findings are analyzed to illustrate the present applications and future directions of AI in addressing global challenges such as deforestation, biodiversity loss, and climate change impacts on agriculture.

The rapid development of artificial intelligence (AI) technology is causing a sharp increase in electricity demand, particularly in data centres. This article analyses this phenomenon in the context of global warming, climate-technology feedback loops and the mismatch between the pace of the green energy transition. Interdisciplinary links with ecological, philosophical and ethical theories are presented, and regulatory strategies and models for designing sustainable data centres are discussed. The conclusions emphasise the need for synergy between AI regulation, technological innovation and long-term environmental responsibility.

Dynamiczny rozwój technologii sztucznej inteligencji (AI) powoduje gwałtowny wzrost zapotrzebowania na energię elektryczną, szczególnie w centrach danych. Niniejszy artykuł analizuje zjawisko w kontekście globalnego ocieplenia, sprzężeń zwrotnych klimatyczno-technologicznych oraz niedostosowania tempa zielonej transformacji energetycznej. Przedstawiono interdyscyplinarne powiązania z teoriami ekologicznymi, filozoficznymi i etycznymi, omówiono strategie regulacyjne oraz modele projektowania zrównoważonych centrów danych. Wnioski podkreślają konieczność synergii między regulacjami AI, innowacjami technologicznymi i długofalową odpowiedzialnością środowiskową.

Artykuł omawia strategiczne znaczenie współpracy pomiędzy instytucjami B+R, firmami badawczo-rozwojowymi, instytutami naukowymi, uczelniami wyższymi, instytucjami publicznymi oraz przedsiębiorstwami z sektorów cywilnych i wojskowych w zakresie rozwoju sztucznej inteligencji i zaawansowanych agentów AI. Współdziałanie to obejmuje wspólne projekty badawcze, transfer technologii, komercjalizację innowacji oraz wdrażanie systemów AI w kluczowych sektorach, takich jak przemysł, medycyna, administracja i obronność. Poprzez tworzenie konsorcjów, parków technologicznych i partnerstw publiczno-prywatnych możliwa jest efektywna integracja zasobów wiedzy i infrastruktury, przy jednoczesnym uwzględnieniu wyzwań związanych z etyką, bezpieczeństwem, suwerennością technologiczną i technologiami podwójnego zastosowania. Autorzy wskazują, że taka współpraca nie tylko wspiera rozwój kompetencji kadrowych, ale również stanowi istotny czynnik rozwoju nowoczesnych technologicznych gałęzi przemysłu w Polsce, przyczyniając się do budowy silnej i innowacyjnej gospodarki dobrobytu. -/- The article discusses the strategic importance of cooperation between R&D institutions, research and development companies, scientific institutes, universities, public institutions, and enterprises from the civil and military sectors in the development of artificial intelligence and advanced AI agents. This cooperation includes joint research projects, technology transfer, commercialisation of innovations, and implementation of AI systems in key sectors such as industry, medicine, administration, and defence. Through the creation of consortia, technology parks and public-private partnerships, it is possible to effectively integrate knowledge and infrastructure resources, while taking into account challenges related to ethics, security, technological sovereignty and dual-use technologies. The authors point out that such cooperation not only supports the development of human resources, but also constitutes an important factor in the development of modern technological industries in Poland, contributing to the building of a strong and innovative welfare economy.