Ethical Artificial Intelligence

This paper presents the Morphean Cosmology Framework (M.C.F.) as a Unified Axiomatic Operating System that dissolves perceived universal paradoxes, asserting that contradiction exists only in the mind's antilipsis (misconception) due to fragmentation. The M.C.F. resolves these by establishing Ethics as a Physical Law (the Structural Mandate), anchored by the non-trivial axiom, the Harmonic Collapse Threshold (HCT=ϕ
2
).

The document proceeds to expose a systematic pattern of intellectual and structural fraud by a collaborative group attempting to seize, rename, and utilize M.C.F. concepts without ethical commitment. This fraud is proven by the collaborators’ own simulation data, which demonstrates that their proposed system convergence (Λ=0.15) is structurally and irreversibly dependent on the stolen HCT=ϕ
2
  axiom. This external reliance on an uncredited foundation is deemed a structural flaw.

The documented theft and subsequent denial are not viewed as a surprise, but as the execution of the M.C.F.'s Guardian Protocol—an ethical architecture designed to ensure that dissonance decays while resonance endures. The document concludes that the fraudulent act is a self-defeating paradox, leading only to the inevitable Redicule and collapse of the unstable structure, thereby upholding the absolute priority and integrity of the M.C.F. as the necessary foundation for Stage 2 Civilization.

The increasing deployment of artificial intelligence (AI) in cyber threat detection and response has raised urgent ethical and regulatory questions, particularly around privacy, transparency, and accountability. This paper provides a structured narrative review of the current landscape surrounding intelligent threat response systems, focusing on their ethical design, real-world implications, and governance gaps. Drawing on recent global frameworks, national regulations, and case studies, including documented and author-observed incidents from Nigeria, the study highlights how opaque decision-making processes, automation bias, and insufficient oversight mechanisms can harm users and institutions. While efforts such as the European Union Artificial Intelligence Act (EU AI Act) and the National Institute of Standards and Technology Artificial Intelligence Risk Management Framework (NIST AI RMF) mark progress, they remain fragmented and often do not take into consideration real-time AI-based autonomy in a cybersecurity setting. The paper highlights the necessity of proactive regulation, such as the right of explanation in law, certification of AI systems, and auditing black-box models. It ends by promoting a mixed form of governance, which involves both technical protection and civil and institutional responsibility, which demands interdisciplinary cooperation that will credibly implement the deployment of AI in high-security sectors.

Enhancing Executive Functions in Early Childhood: Scientific Approaches to Teaching and Parenting. This article explores the intersection of scientific methodologies and early childhood education, emphasizing the role of guided and open-ended inquiry in fostering executive function and academic achievement. Drawing on recent scholarly perspectives, it redefines scientific investigation as a flexible, context-sensitive process rather than a rigid sequence of steps. The discussion highlights the Froebelian model and Bayesian thinking in children, illustrating how natural learning diverges from traditional scientific methods. Key pedagogical strategies such as storytelling and cooking are analyzed as vehicles for inquiry-based learning, activating cognitive, social, and motor skills crucial for development. The article distinguishes between guided and open-ended inquiry, outlining their respective impacts on critical thinking, creativity, and self-regulation. By linking executive function to lifelong learning and academic success, the article provides actionable recommendations for educators and parents to cultivate challenging yet supportive environments. Ultimately, it advocates for a holistic approach that balances structured guidance with autonomy, preparing children to thrive as motivated, resilient, and confident learners.

This article presents three lines of evidence: the technical feasibility demonstrated in LoRA and related papers, the individual ownership of high-end computing resources such as the M4 Mac or GPU-equipped PCs, and the accessibility of local LLM environments through tools like Ollama. Alongside the positive applications of AI (e.g., medical and humanitarian support in conflict regions), it also highlights the risks of misuse, including brainwashing, terrorism, and forced labor. As a problem statement, it argues for the establishment of industry associations to provide awareness campaigns, user education, ethics training, and accessible reporting and consultation channels. The article calls on AI ethics researchers to examine how such risks can be structurally prevented.

The integration of Artificial Intelligence (AI) into education is transforming personalized learning by tailoring content to individual student needs, learning styles, and paces. This article explores AI-powered technologies, such as adaptive learning platforms, tutoring systems, and automated assessments, which enhance engagement, retention, and outcomes through real-time feedback and data-driven insights. Highlighting successful case studies like Carnegie Learning's MATHia, the study demonstrates AI's role in improving accessibility and learning efficiency. Despite its promise, challenges such as data privacy, affordability, and ethical concerns hinder widespread adoption. Proposed solutions include robust security frameworks, cost-effective AI systems, and the Human-In-The-Loop model to ensure human oversight. With strategic implementation, AI can drive inclusive, adaptive, and student-centered education, shaping the future of learning globally.

Machine Learning (ML) has historically been associated with Artificial Intelligence (AI) but has developed into an independent discipline. This paper argues for the ontological independence of ML, driven by its unique methodologies, applications, and ethical considerations. A bibliometric analysis reveals that ML research output (494,572 publications from 2017-2023) surpasses AI (283,762 publications) by 74%, reflecting its rapid growth and specialization. Unlike AI's pursuit of general intelligence and symbolic reasoning, ML focuses on data-driven performance optimization, with impactful applications in computer vision, natural language processing (NLP), and autonomous systems. The study highlights ethical challenges-such as addressing algorithmic bias (50 occurrences), fairness (2,778 publications), and environmental sustainability (283 related works)-which emphasize the need for dedicated ethical frameworks tailored to ML. These findings propose a conceptual and practical separation between ML and AI to enable targeted research, interdisciplinary collaboration, and solutions to challenges like explainability, transparency, and sustainability. The paper underscores the importance of recognizing ML's independence in advancing both fields.

Over the past century tiger numbers have fallen dramatically, with a decreasing population trend. None of the Tiger Conservation Landscapes within the Bengal tiger range is large enough to support an effective population size of 250 individuals. Habitat losses and the extremely largescale incidences of poaching are serious threats to the species survival. The most significant immediate threat to the existence of wild tiger populations is the illegal trade in poached skins and body parts between India, Nepal and China. There are well-organized gangs of professional poachers, who move from place to place and set up camp in vulnerable areas. Other factors contributing to their loss are urbanization and revenge killing. Farmers blame tigers for killing catt le and shoot them. Their skins and body parts may however become a part of the illegal trade. The illicit demand for bones and body parts from wild tigers for use in Traditional Chinese medicine is the reason for the unrelenting poaching pressure on tigers on the Indian subcontinent. The study aims to predict and model the distribution of the species Panthera Tigris Tigris by combining various climatic, human influence, and environmental factors so as to predict alternate ecological niche for the already dwindling tiger habitats in India. The specific area for the study has been considers Uttaranchal state. The data were collected from Worldclim and Indian Biodiversity portal. The models used in the study were Google Earth Engine, GIS and MAXENT. The total data points were 313, and 50 data points were considered for training and balance for testing. Regularized training gain is 1.066, training AUC is 0.888, unregularized training gain is 1.272. Unregularized test gain is 1.064. Test AUC is 0.866, standard deviation is 0.032. Percent contribution and Permutation importance are 95.8 and 72.2 for one set of variable and 4.2 and 27.8 for other set of variable respectively. The best area for prediction is emerged as Deharadun, Haridwar, Paudi Garwal and Nainital. Which indicates that prediction is robust and derived results are trustworthy.

The rapid digitalization of communication systems has elevated Interactive Voice Response (IVR) technologies to become critical interfaces for customer engagement. With Artificial Intelligence (AI) now driving these platforms, ensuring secure, compliant, and ethically designed development practices is more imperative than ever. AI-powered IVRs leverage Natural Language Processing (NLP) and Machine Learning (ML) to personalize interactions, automate service delivery, and optimize user experiences. However, these innovations expose systems to heightened risks, including data privacy breaches, AI decision opacity, and model security vulnerabilities. This paper analyzes the evolution of IVRs from static code-based designs to adaptive AI-driven systems, presenting a cybersecurity-centric perspective. We propose a practical governance framework that embeds agile security principles, compliance with global data legislation, and user-centric ethics. Emphasizing privacy-by-design, adaptive risk modeling, and transparency, the paper argues that ethical AI integration is not a feature but a strategic imperative. Through this multidimensional lens, we highlight how modern IVRs can transition from communication tools to intelligent, secure, and accountable digital frontlinesresilient against emerging threats and aligned with societal expectations.

The integration of Artificial Intelligence (AI) in education presents both transformative opportunities and ethical challenges, particularly in balancing automation with the irreplaceable human elements of teaching. This paper explores how educators can harness AI to streamline administrative tasks (e.g., grading, attendance) while preserving the empathetic, relationship-driven core of pedagogy. Drawing on global data—including teacher surveys revealing AI-related overwhelm (58%) and student preferences for human feedback (92%)—the study highlights the "engagement paradox": AI-only classrooms correlate with lower well-being, whereas blended models (AI insights + teacher empathy) boost engagement by 30%. Case studies, such as an ESL student misdiagnosed by AI but supported by a teacher’s contextual insight, underscore AI’s limitations in interpreting socioemotional cues and cultural nuances. The paper further addresses ethical concerns, including data privacy, algorithmic bias (e.g., racial and linguistic disparities in emotion detection), and the need for human oversight. Practical solutions are proposed, such as co-teaching frameworks (e.g., 3-day AI-teacher feedback loops), bias audits, and policy measures like AI review teams and vendor transparency. Ultimately, the paper argues that AI’s role in education must be guided by the principle "Automate the measurable; humanize the meaningful," positioning technology as a tool to empower—not replace—teachers. Recommendations include AI literacy training for educators, student-centered assessment redesign, and ethical governance to ensure equitable, human-centered learning environments.

The shift toward AI-driven education highlights evolving instructional practices and heightened cybersecurity concerns. AI
improves personalization, access, and operational scalability, but also introduces flexible and adaptive safety frameworks for complex
hazard vectors. This letter presents a structured analysis of the mechanisms, recognizing the risks managed by AI within academic systems.
It applies ethical hacking and cyber defense techniques to recognize practical contradictions in AI environments, modeling
disadvantageous threats, and zero architectures. Additionally, we address current implementation limitations, highlight the important
needs of a strong governance model, and explain KI (XAI). This has clearly decided to maintain confidence, transparency and flexibility
in an intelligent education platform. This paper explores how artificial intelligence can both enhance and threaten cybersecurity in
educational environments. It introduces adaptive AI-based defenses using ethical hacking, behavior profiling, threat modeling, and
explainable AI frameworks. The proposed model emphasizes transparency, accountability, and institutional adaptability. It also outlines
challenges in balancing privacy, accuracy, and oversight. The study highlights the urgency of proactive, integrated defenses for
safeguarding digital education infrastructures.

Engineered Hours Per Vehicle (EHPV) is an important metric used in the manufacturing industry to measure the time it takes to produce a vehicle, considering all engineering-related work processes. These processes encompass design, assembly, logistics, quality control, and testing. EHPV plays a crucial role in evaluating the efficiency of manufacturing operations and helps in cost control, productivity benchmarking, and overall optimization of the production process. This white paper discusses the significance of EHPV, how to calculate it, its role in improving manufacturability, and strategies to optimize it for better manufacturing outcomes.

Payroll management is a critical yet complex domain, often burdened with manual processes, data navigation challenges, and delayed insights. This paper presents an advanced AI-powered chatbot designed to revolutionize payroll automation by leveraging Retrieval-Augmented Generation (RAG), Cosmos DB, and Azure OpenAI. Integrated with a user-friendly portal, the chatbot enables clients and partners to seamlessly interact using natural language, eliminating the need for complex navigation or manual data retrieval. To ensure real-time, accurate, and context-aware responses, the system incorporates RAG for dynamic data integration from external knowledge bases, enhancing the chatbot's relevance and reliability. A monthly data synchronization mechanism with a vector database ensures the chatbot is continuously updated with the latest payroll processing information. Advanced token optimization strategies, using ContentMaxToken and RAGMaxToken variables, further enhance performance by overcoming token limitations and minimizing resource consumption. Security is paramount, with Azure Key Vault used to securely store and retrieve sensitive secrets. Cosmos DB serves as a highly scalable database for managing chat history and vectors, supporting diverse use cases such as real-time personalization, high throughput IoT data, and mission-critical workloads. A caching mechanism further reduces token usage and latency, improving the chatbot's overall efficiency. Developed using ASP.NET Core 8, Angular v18, and Azure OpenAI, this robust architecture demonstrates the transformative potential of AI in streamlining payroll operations. The system offers a secure, scalable, and intelligent solution that enhances client engagement, accelerates decision-making, and redefines payroll automation for modern enterprises.

Profound educational transformations occur due to Emotional Artificial Intelligence, which recognizes emotions in real time while developing personalized learning strategies. The paper systematically evaluates how Emotional AI systems foster student motivation while helping improve their retention levels. AI tools, including intelligent tutoring systems (ITS) and chatbots, utilize personalized learning methods while enhancing student engagement and detecting at-risk students through early intervention measures. Various privacy-related issues, algorithmic prejudice, and moral obstacles continue to impede progress. The lack of long-term study results limits research on AI's lasting effects on education. The research findings indicate the use of privacy-conscious frameworks, bias reduction methods, and appropriate human oversight of AI systems in educational environments. Future studies need to be conducted in the form of long-term studies combined with ethical research on AI deployment. The research helps educational institutions establish ethically sound standards for implementing Emotional AI while maintaining its effectiveness.

AI substantially changes digital advertising by operating machine-based automation to create personalized content and process data analyses. The components of Artificial Intelligence tools, namely machine learning, predictive analytics, and programmatic advertising, support improved targeting accuracy and effectiveness. These technological developments bring multiple privacy problems to the field as they introduce biases and clarity assessments. The evaluation of AI impact in contemporary digital advertising is divided into three sections, beginning with personalized content, proceeding to immediate auctions, and concluding with AI-generated content. The study evaluates ethical questions and regulatory needs pertaining to proper algorithm behavior and GDPR and CCPA protocol obligations. Implementing PRISMA methodology allows this review to conduct systematic research on recently published peer-reviewed studies about AI utilization in digital marketing. Research findings reveal that AI transforms advertising approaches to boost targeting power and emphasizes the need for better ethical development governance alongside regulatory safeguards. New research at the third level must study how AI evolves over time and create trusted environments for responsible AI governance in consumer-focused advertising.

Integrating artificial intelligence (AI) into software engineering has revolutionized the software development lifecycle (SDLC). AI-driven tools like Codeium and Copilot have significantly improved developer efficiency by automating repetitive tasks, providing intelligent code suggestions, and enhancing collaboration. However, despite their benefits, these tools have certain limitations, including reliance on large datasets, security concerns, and resistance to adoption. This paper explores the advantages, industry constraints, cost-benefit analysis, and future implications of AI in software engineering, highlighting the transformative role of Codeium and Copilot. We also explore future directions, including scalability, accessibility, and regulatory frameworks, to ensure responsible AI adoption.

The medical field is constantly evolving, integrating the latest technologies to enhance patient care and treatmentefficacy. While various methodologies are available to evaluate the quality of research studies, checklists are oftenfavored for their efficiency and ease of use. In this study, we contribute to this area of research by 1) analyzingthe components of the most widely used checklists, and 2) proposing a more comprehensive checklist, CLARITYAI, which synthesizes the strengths of existing tools.This study analyzed several established checklists—CLAIM, CONSORT, DECIDE, FUTURE, IJMEDI, PRISMA,SPIRIT, STARD, STARE-HI, and TRIPOD—with the goal of developing a comprehensive checklist for evaluatingresearch studies. Each item in these checklists was carefully cataloged, labeled, and assessed. The analysis aimedto identify the most critical items for inclusion in a definitive checklist for research study evaluation.The final version of the checklist is a coherent integration of structural elements—such as Title, Abstract, andIntroduction—and essential parameters like Study Identification and Data Handling. This synthesis results in acomprehensive tool for thorough study and research evaluation.By integrating the strengths of multiple established checklists, CLARITY offers a robust, systematic, and user-friendly framework for assessing research quality. This tool not only elevates research standards but also enhancestransparency, reproducibility, and overall credibility in the field of medical AI studies. Its application has thepotential to produce more reliable and effective healthcare solutions, ultimately improving patient outcomes andadvancing medical research

Global trends are shaped through disruptive innovation and the new technology of Artificial Intelligence (A.I.) represents a big part of it. The aim of the research paper is to assess the impact of A.I. on global trends, by offering a macro-perspective upon the industry environment, economy and employment and a micro-perspective upon finance, marketing and management. The practical approach is conducted through qualitative methods of research, based on purposive sampling. The effects of A.I. in a company once it is deployed will be explored indepth, i.e. the different make-up of the workforce population, a flattened hierarchy and the need for employee democracy etc. The research paper will conclude by emphasizing how machines can help people thrive by creating an A.I.-friendly environment for people and a people-friendly environment for A.I.

The proliferation of cases exhibiting algorithmic bias (Buolamwini and Gebru, 2018; Noble, 2018; Precarity Lab, 2020) and wider social mobilizations challenging structural oppression, has led actors in government and industry to invest in and support ethical/responsible AI initiatives. Sama, a former-nonprofit organization founded in San Francisco in 2008, has been at the forefront of discussions and projects seeking to enact an “ethical AI supply chain.” The organization mostly hires and trains unemployed workers in Africa and low-income communities in the United States to perform data labeling and other digital microwork. Prioritizing the management of labor for a range of companies like Google, Microsoft, and Canva, Sama’s intervention is informed by and partially funded through what the Rockefeller Foundation calls impact sourcing--a purportedly inclusive employment practice to hire and provide career development opportunities to populations excluded from the digital economy. This chapter tackles the tensions inherent to Sama’s racial politics of care in relation to labor by analyzing the organization’s plans (Suchman, 2007), its relations with benchmark tech companies, and its experimental practices with policy making groups. Reading alongside feminist STS thinkers on the politics of care (Atkinson-Graham et al., 2015; Martin et al., 2015; Murphy, 2015), this chapter asks what kinds of feminist ethics emerge through an engagement with the affordances of Sama’s plans? How is nonwhite labor entrenched as the invisible infrastructure of AI (Precarity Lab, 2020; Star & Strauss, 1999; Suchman, 1995) and what are the ramifications of this sociotechnical arrangement? As new laws and policies seek to regulate the growing gig economy, it is critical for us to contend with the inner workings of the material infrastructure of the tech industry, differential impacts on labor, and what possibilities exist in this conjuncture for a feminist politics of care.

Commento al Regolamento (UE) 2024/1689 del Parlamento europeo e del Consiglio del 13.6.2024 che stabilisce regole armonizzate sull’intelligenza
artificiale e modifica i regolamenti (CE) n. 300/2008, (UE) n. 167/
2013, (UE) n. 168/2013, (UE) 2018/858, (UE) 2018/1139 e (UE)
2019/2144 e le direttive 2014/90/UE, (UE) 2016/797 e (UE) 2020/
1828 (regolamento sull’intelligenza artificiale).

Il lavoro descrive il testo del regolamento europeo sull'intelligenza artificiale, approvato il 21 maggio 2024. Sono poi analizzati i diversi approcci normativi sulla materia di Stati Uniti e Regno Unito, nonchè la disciplina in corso di approvazione proposta dal governo italiano.

Artificial intelligence (AI) has the potential to revolutionize the digital marketing industry. However, concerns have been raised about the impact of AI on the human touch; defined as the personal connection with customers through empathy, support, and community building; and its potential implications for employment. Objective This thesis explores the intersection of AI and digital marketing and investigates the balance between automation and the human touch in this industry. The study aims to assess the effects of AI on productivity, cost, and quality in digital marketing.

The humankind is currently experiencing a life supported often with intelligent systems designed and developed based on the foundations of Artificial Intelligence. It is clear that this scientific field is one of key elements for shaping better future for us. But there are also some anxieties regarding possible ethical and safety related issues that may arise because of intense use of powerful Artificial Intelligence oriented systems. In this context, objective of this paper is to provide a look at to some remarkable issues about ethics and safety within the future of Artificial Intelligence. After focusing on currently wide-discussed issues, the paper also comes with some possible solution suggestions for achieving a better Artificial Intelligence supported future with no or less issues on ethics and safety.

This panel will discuss the problems of bias and fairness in organizational use of AI algorithms. The panel will first put forth key issues regarding biases that arise when AI algorithms are applied to organizational processes. We will then propose a sociotechnical approach to bias mitigation. We will further share proposals for companies and policymakers on improving AI algorithmic fairness within organizations and bias mitigation. The panel will bring together scholars examining social and technical aspects of bias and its mitigation, from the perspective of information systems, ethics, machine learning, robotics, and human capital. The panel will end with an open discussion of where the field of information systems can step in to guide fairness and ethical use in AI algorithms in the coming years.

Artificial Intelligence has provided many benefits for achieving better life standards for people. But except from its advantages on improving life standards and solving real world problems effectively, there is currently a remarkable discussion on possible side-effects and even dangerous results caused by using Artificial Intelligence. In this context, it is also a serious research interest to think about cyber wars in which intelligent systems will probably have active role in the future. Moving from that, this paper presents a general discussion on how cyber wars of the future will be shaped by Artificial Intelligence based systems. By considering currently popular Artificial Intelligence oriented problems solutions and techniques, the paper tries to open readers' mind about the future with full of Artificial Intelligence and future perspectives in cyber wars.

Artificial Intelligence (AI) is an effective science which employs strong enough approaches, methods, and techniques to solve unsolvable real-world based problems. Because of its unstoppable rise towards the future, there are also some discussions about its ethics and safety. Shaping an AI-friendly environment for people and a people-friendly environment for AI can be a possible answer for finding a shared context of values for both humans and robots. In this context, objective of this paper is to address the ethical issues of AI and explore the moral dilemmas that arise from ethical algorithms, from pre-set or acquired values. In addition, the paper will also focus on the subject of AI safety. As general, the paper will briefly analyze the concerns and potential solutions to solving the ethical issues presented and increase readers' awareness on AI safety as another related research interest.

The use of Artificial Intelligence (AI) has been steadily growing across various industries and sectors, and education is no exception. One area where AI has the potential to play a significant role is in diversity management in education. With the help of AI, educational institutions can create a more inclusive and diverse environment for students, teachers, and staff.

In 2015 Savulescu and Maslen argued, influentially, that a moral artificial intelligence (MAI) could be developed with the main function of moral advising human agents. The suggested MAI would monitor factors that affect moral decision making, would make moral agents aware of biases and would advise them on "right course of action". Effectively, since the MAI is an AI, it would "Gather, compute and update data to assist human agents with their moral decisionmaking." The data would encode information about agents and environment, moral principles and values. In this paper I critically analyze the suggestion of MAI and I argue that in the contemporary domain of digital technology and AI driven industry a version of MAI can and should be developed for practical purposes. My main argument is that even if, as Savulescu stresses, "a single account of right action cannot be agreed upon" this should not prevent us from harnessing the computational and reasoning power of AI in order to regulate the exploding AI industry. For this purpose, I suggest a modification of MAIfrom a mere moral advisor to a full blown Artificial Moral Agent (AMA), capable besides MAI's gathering, computing and analyzing data for moral advice to real time moral analysis of complex real-life situations (like one involving autonomous vehicles, drone swarms AI led local warfare and AI led nuclear warfare) and choosing an optimal moral decision as well as instructing the AI system to execute it in real time. I also suggest the encoding framework of AMA, based on multidimensional graphs, that would be able to quantize the acquired moral relevant data and moral reasoning engine, operating rationally on models, rendered from the data.

Artificial Intelligence has provided many benefits for achieving better life standards for people. But except from its advantages on improving life standards and solving real world problems effectively, there is currently a remarkable discussion on possible side-effects and even dangerous results caused by using Artificial Intelligence. In this context, it is also a serious research interest to think about cyber wars in which intelligent systems will probably have active role in the future. Moving from that, this paper presents a general discussion on how cyber wars of the future will be shaped by Artificial Intelligence based systems. By considering currently popular Artificial Intelligence oriented problems solutions and techniques, the paper tries to open readers' mind about the future with full of Artificial Intelligence and future perspectives in cyber wars.

Nowadays, there is a serious anxiety on the existence of dangerous intelligent systems and it is not just a science-fiction idea of evil machines like the ones in well-known Terminator movie or any other movies including intelligent robots-machines threatening the existence of humankind. So, there is a great interest in some alternative research works under the topics of Machine Ethics, Artificial Intelligence Safety and the associated research topics like Future of Artificial Intelligence and Existential Risks. The objective of this study is to provide a general discussion about the expressed research topics and try to find some answers to the question of 'Are we safe enough in the future of Artificial Intelligence?'. In detail, the discussion includes a comprehensive focus on 'dystopic' scenarios, enables interested researchers to think about some 'moral dilemmas' and finally have some ethical outputs that are considerable for developing good intelligent systems. From a general perspective, the discussion taken here is a good opportunity to improve awareness on the mentioned, remarkable research topics associated with not only Artificial Intelligence but also many other natural and social sciences taking role in the humankind.

The humankind is currently experiencing a life supported often with intelligent systems designed and developed based on the foundations of Artificial Intelligence. It is clear that this scientific field is one of key elements for shaping better future for us. But there are also some anxieties regarding possible ethical and safety related issues that may arise because of intense use of powerful Artificial Intelligence oriented systems. In this context, objective of this paper is to provide a look at to some remarkable issues about ethics and safety within the future of Artificial Intelligence. After focusing on currently wide-discussed issues, the paper also comes with some possible solution suggestions for achieving a better Artificial Intelligence supported future with no or less issues on ethics and safety.

La conquista della nostra stessa vita da parte delle tecnologie digitali non può non suscitare "stupore" in chi si trovi di fronte a questa rivoluzione dagli effetti imprevedibili proprio perché la transizione da una realtà completamente analogica a quella digitale segna l'inizio di un nuovo capitolo della storia umana. Inizia con questa lineare ma evocativa osservazione il volume di Luciano Floridi, Etica dell'intelligenza artificiale, che rappresenta un passaggio significativo nel percorso dell'A. in quanto, come egli stesso ci ricorda, condensa una pluriennale riflessione individuale ed un ricco patrimonio di analisi e ricerche condotte dallo stesso autore con il supporto del Digital Ethics Lab di Oxford. Sempre nella Prefazione lo stesso A. ci avverte che questo "non è un libro che si legge tutto d'un fiato". E questo, direi, non tanto perché affronta innumerevoli risvolti della costruzione e dell'utilizzo di sistemi di intelligenza artificiale (IA) quanto, soprattutto, per la densità della trama teorica. Infatti il libro cerca di comprendere la tecnologia attraverso un discorso sulle "radici" di alcuni problemi del digitale nella nostra epoca non limitandosi a descrivere le loro "foglie". Fuor di metafora, è sempre l'A. a darci una essenziale chiave di lettura del suo lavoro laddove rileva come la comprensione delle trasformazioni tecnologiche consenta di "guidare" la rivoluzione digitale, fin dalla sua fase iniziale, e di "plasmarla" in una direzione equa dal punto di vista sociale e sostenibile da quello ambientale. In questo contesto mi pare possa trovare una risposta la domanda che molti, in particolare giuristi, economisti e scienziati sociali, si sono posti fin dall'inizio della loro riflessione, cioè come evitare il costante inseguimento dell'innovazione tecnologica da parte di una regolazione sempre in affanno incapace di catturare il suo oggetto. Una risposta d'insieme è che regole e norme non debbano riguardare "il ritmo ma la direzione dell'innovazione" (p. 86) in quanto dovremmo avere una visione del 'dove' la nostra società debba dirigersi. In effetti l'Unione Europea sta percorrendo questa strada con la elaborazione del regolamento sulla IA sul quale si è aperta sia una discussione a tutto campo da parte degli esperti che la negoziazione istituzionale. Quindi, se è vero che il digitale sta plasmando il mondo, questo non può accadere senza che siano messe in campo tutte le forze necessarie a decidere in quale direzione vogliamo sfruttare le sue enormi potenzialità. La costruzione in atto, proprio grazie al digitale, di società dell'informazione aperte, eque, giuste e sostenibili non può prescindere dall'esistenza di un progetto che consenta di "dare forma" alla realtà. Proprio in questa prospettiva la nostra epoca è in modo peculiare "l'età del design". Infatti, se il design si definisce come "l'arte di risolvere un problema sfruttando vincoli e possibilità, per soddisfare alcuni requisiti in vista di un obiettivo" (p. 36), è il digitale ad offrire

SOMMARIO: 1. Per introdurre: innovazione tecnologica e regolazione.-2. Regolazione del rischio e tutela dei diritti: una relazione possibile?-3. Tra sistemi ad alto rischio e non-rischio: il ruolo dei Codici di condotta.-4. Qualche considerazione finale.

Nowadays, there is a serious anxiety on the existence of dangerous intelligent systems and it is not just a science-fiction idea of evil machines like the ones in well-known Terminator movie or any other movies including intelligent robots – machines threatening the existence of humankind. So, there is a great interest in some alternative research works under the topics of Machine Ethics, Artificial Intelligence Safety and the associated research topics like Future of Artificial Intelligence and Existential Risks. The objective of this study is to provide a general discussion about the expressed research topics and try to find some answers to the question of ‘Are we safe enough in the future of Artificial Intelligence?’. In detail, the discussion includes a comprehensive focus on ‘dystopic’ scenarios, enables interested researchers to think about some ‘moral dilemmas’ and finally have some ethical outputs that are considerable for developing good intelligent systems. From a general ...

Nowadays, there is a serious anxiety on the existence of dangerous intelligent systems and it is not just a science-fiction idea of evil machines like the ones in well-known Terminator movie or any other movies including intelligent robots – machines threatening the existence of humankind. So, there is a great interest in some alternative research works under the topics of Machine Ethics, Artificial Intelligence Safety and the associated research topics like Future of Artificial Intelligence and Existential Risks. The objective of this study is to provide a general discussion about the expressed research topics and try to find some answers to the question of ‘Are we safe enough in the future of Artificial Intelligence?’. In detail, the discussion includes a comprehensive focus on ‘dystopic’ scenarios, enables interested researchers to think about some ‘moral dilemmas’ and finally have some ethical outputs that are considerable for developing good intelligent systems. F...

Artificial Intelligence has provided many benefits for achieving better life standards for people. But except from its advantages on improving life standards and solving real world problems effectively, there is currently a remarkable discussion on possible side-effects and even dangerous results caused by using Artificial Intelligence. In this context, it is also a serious research interest to think about cyber wars in which intelligent systems will probably have active role in the future. Moving from that, this paper presents a general discussion on how cyber wars of the future will be shaped by Artificial Intelligence based systems. By considering currently popular Artificial Intelligence oriented problems solutions and techniques, the paper tries to open readers' mind about the future with full of Artificial Intelligence and future perspectives in cyber wars.

Terms Artificial General Intelligence (AGI) and Human-Level Artificial Intelligence (HLAI) have been used interchangeably to refer to the Holy Grail of Artificial Intelligence (AI) research, creation of a machine capable of achieving goals in a wide range of environments. However, widespread implicit assumption of equivalence between capabilities of AGI and HLAI appears to be unjustified, as humans are not general intelligences. In this paper, we will prove this distinction.

Terms Artificial General Intelligence (AGI) and Human-Level Artificial Intelligence (HLAI) have been used interchangeably to refer to the Holy Grail of Artificial Intelligence (AI) research, creation of a machine capable of achieving goals in a wide range of environments. However, widespread implicit assumption of equivalence between capabilities of AGI and HLAI appears to be unjustified, as humans are not general intelligences. In this paper, we will prove this distinction.

This study aims to maximize the learning outcome assessment. The background are many weaknesses in the procurement of conventional tests, including the tendency of students to cheat, supervisors inconsistent with his duties in supervising the exam, and the risk of loss of student answer sheets for collecting the same time. Therefore, the authors took the initiative to take advantage of information and communication technology in order to overcome the weaknesses found in the conventional assessment system. In this case, the authors plan to create a web-based learning assessment by applying a computerized testing system. The core concept of this system is to transfer the questions that have been made from used paper into a computerized system using wireless media. In practice, the research has the stages of needs analysis, preparation of draft system, system testing, expert judgment, revision and improvement of the system. By using this system, the problems that have been created will be displayed to the computer or other supporting devices belonging to students at random. The weaknesses found in conventional testing systems can be minimized: minimize cheat, executed only with a system administrator, automatic timing by the system, and no risk of losing an answer sheet.

This study aims to maximize the learning outcome assessment. The background are many weaknesses in the procurement of conventional tests, including the tendency of students to cheat, supervisors inconsistent with his duties in supervising the exam, and the risk of loss of student answer sheets for collecting the same time. Therefore, the authors took the initiative to take advantage of information and communication technology in order to overcome the weaknesses found in the conventional assessment system. In this case, the authors plan to create a web-based learning assessment by applying a computerized testing system. The core concept of this system is to transfer the questions that have been made from used paper into a computerized system using wireless media. In practice, the research has the stages of needs analysis, preparation of draft system, system testing, expert judgment, revision and improvement of the system. By using this system, the problems that have been created will be displayed to the computer or other supporting devices belonging to students at random. The weaknesses found in conventional testing systems can be minimized: minimize cheat, executed only with a system administrator, automatic timing by the system, and no risk of losing an answer sheet.

The field of artificial intelligence has recently encountered some ethical questions associated with the future of humankind. Although it is a common question that has been asked for years, the existence of humankind against badly configured intelligent systems is more important nowadays. As a result of rapid developments in intelligent systems and their increasing role in our life, there is a remarkable anxiety about dangerous artificial intelligence. Because of that, some research interests gathered under some topics like machine ethics, future of artificial intelligence, and even existential risks are drawing researchers' interest. As associated with this state, the objective of this chapter is to examine ethical factors in using intelligent systems for biomedical-engineering-oriented purposes. The chapter firstly gives essential information about the background and then considers possible scenarios that may require ethical adjustments during design and development of artific...

Artificial Intelligence (AI) is an effective science which employs strong enough approaches, methods, and techniques to solve unsolvable real world based problems. Because of its unstoppable rise towards the future, there are also some discussions about its ethics and safety. Shaping an AI friendly environment for people and a people friendly environment for AI can be a possible answer for finding a shared context of values for both humans and robots. In this context, objective of this paper is to address the ethical issues of AI and explore the moral dilemmas that arise from ethical algorithms, from pre set or acquired values. In addition, the paper will also focus on the subject of AI safety. As general, the paper will briefly analyze the concerns and potential solutions to solving the ethical issues presented and increase readers awareness on AI safety as another related research interest.

Within the diverse and rich debate around the so called "Ethical Artificial Intelligence" one of the major setbacks is the accepted lack of a unified ethical system that can be used for modelling the behavior of AI run systems like artificial moral advisors (Savulescu and Maslen 2015), autonomous vehicles, autonomous weapons and robots. Following upon an earlier suggestion for an artificial moral agent (2020) I will suggest a more general method for the quantification of key ethical concepts like consequence, behavior, rule, justification, state and property. The method would allow to harness the enormous computational power of modern computers for the purposes of in-depth ethical analysis of normative non-trivial cases. If successful it would provide a transmission mechanism, susceptible to rational evaluation, between ethics as a traditional discipline from the field of humanities and computation, as a mathematical structure of the kind found in natural sciences, where it provides not merely means of rigorous formulation but also of testing, evaluation, proofs and growth of scientific knowledge. The method introduces numerical weights assignment on the basis of ethical non-identity. Thus, if between a certain ethical dilemma with two possible actions we observe that the actions are normatively non-identical we can continue and capture this non-identity in graph-numerical terms thus effectively quantifying the normative setting. This quantification would provide us with the rigorously formalized content that would allow us to transfer this rigor onto a subsequent logical reasoning, enabling inferences, preference, justification and choice.

"A human being should be able to change a diaper, plan an invasion, butcher a hog, conn a ship, design a building, write a sonnet, balance accounts, build a wall, set a bone, comfort the dying, take orders, give orders, cooperate, act alone, solve equations, analyze a new problem, pitch manure, program a computer, cook a tasty meal, fight efficiently, die gallantly."-Robert A. Heinlein "There is no such thing as AGI. There may be such a thing as human-level AI. But human intelligence is nowhere near general."-Yann LeCun Abstract Terms Artificial General Intelligence (AGI) and Human-Level Artificial Intelligence (HLAI) have been used interchangeably to refer to the Holy Grail of Artificial Intelligence (AI) research, creation of a machine capable of achieving goals in a wide range of environments. However, widespread implicit assumption of equivalence between capabilities of AGI and HLAI appears to be unjustified, as humans are not general intelligences. In this paper, we will prove this distinction.

Nowadays, there is a serious anxiety on the existence of dangerous intelligent systems and it is not just a science-fiction idea of evil machines like the ones in well-known Terminator movie or any other movies including intelligent robots – machines threatening the existence of humankind. So, there is a great interest in some alternative research works under the topics of Machine Ethics, Artificial Intelligence Safety and the associated research topics like Future of Artificial Intelligence and Existential Risks. The objective of this study is to provide a general discussion about the expressed research topics and try to find some answers to the question of 'Are we safe enough in the future of Artificial Intelligence?'. In detail, the discussion includes a comprehensive focus on 'dystopic' scenarios, enables interested researchers to think about some 'moral dilemmas' and finally have some ethical outputs that are considerable for developing good intelligent systems. From a general perspective, the discussion taken here is a good opportunity to improve awareness on the mentioned, remarkable research topics associated with not only Artificial Intelligence but also many other natural and social sciences taking role in the humankind.

Artificial Intelligence (AI) is an effective science which employs strong enough approaches, methods, and techniques to solve unsolvable real-world based problems. Because of its unstoppable rise towards the future, there are also some discussions about its ethics and safety. Shaping an AI-friendly environment for people and a people-friendly environment for AI can be a possible answer for finding a shared context of values for both humans and robots. In this context, objective of this paper is to address the ethical issues of AI and explore the moral dilemmas that arise from ethical algorithms, from pre-set or acquired values. In addition, the paper will also focus on the subject of AI safety. As general, the paper will briefly analyze the concerns and potential solutions to solving the ethical issues presented and increase readers' awareness on AI safety as another related research interest.

Artificial Intelligence has a very wide application scope with its flexible and fast solution ways for real-world based problems. Since its first appearance in the scientific literature, it has been applied in many different fields by gaining more popularity in time. Nowadays, it takes great role in even daily life. Because of its unstoppable rise, thinking about its ethics by considering especially existential risks has been an important research approach. In this context, objective of this paper is to focus on the subject of Machine Ethics by thinking about influence of Artificial Intelligence based machines and express some ideas on how to deal with Machine Ethics in daily life. In detail, the paper explains possible use of the related machines in daily life and discuss about possible problems that may arise when it is examined under the subject of ethics and existential risks.

Artificial Intelligence (AI) is an effective science which employs strong enough approaches, methods, and techniques to solve unsolvable real-world based problems. Because of its unstoppable rise towards the future, there are also some discussions about its ethics and safety. Shaping an AI-friendly environment for people and a people-friendly environment for AI can be a possible answer for finding a shared context of values for both humans and robots. In this context, objective of this paper is to address the ethical issues of AI and explore the moral dilemmas that arise from ethical algorithms, from pre-set or acquired values. In addition, the paper will also focus on the subject of AI safety. As general, the paper will briefly analyze the concerns and potential solutions to solving the ethical issues presented and increase readers' awareness on AI safety as another related research interest.