AI and BIAS

The integration of Artificial Intelligence (AI) in the financial sector has raised ethical concerns that require attention. This research aims to analyze the ethical implications of AI usage in financial decision-making. The paper emphasizes the need for an ethical framework to ensure the fair and trustworthy deployment of AI, addressing issues such as algorithmic bias, transparency, and regulatory compliance regarding fairness, accountability, and transparency. A comprehensive ethical framework is proposed to manage these challenges and responsibly leverage AI in the financial sector, including tackling algorithmic bias, promoting transparency, and ensuring compliance with regulations that protect fairness, accountability, and public trust. This paper examines the complex relationship between AI innovations and ethical integrity in the financial sector by combining research and empirical evidence. It proposes a practical and comprehensive ethical framework, including clear guidelines, governance structures, regular audits, and stakeholder collaboration. The goal of this framework is to harness the potential of AI while minimizing its negative impacts and unintended consequences. This study serves as a valuable resource for policymakers, professionals, researchers, and other stakeholders, contributing to informed discussions, evidence-based decision-making, and the development of best practices for the responsible integration of AI in the financial sector. Ultimately, the aim is to ensure fairness, transparency, and accountability while maximizing the benefits of AI for both the financial sector and society.

The swift development of artificial intelligence (AI) and the appearance of selfgoverning "digital entities" offer modern societies previously unheard-of possibilities as well as dangers. AI presents moral, legal, and sociopolitical conundrums even as it promises to increase productivity, enhance decision-making, and solve difficult global issues. These include the potential for developing autonomous systems whose behavior may surpass human oversight, algorithmic bias, the concentration of technological power in a small number of corporations, and the opacity of decisionmaking processes. Furthermore, the increasing discussion of digital entities as quasiautonomous actors-whether they be bots, algorithmic agents, or avatars-needs a quick analysis of the implications for governance, accountability, and human rights. This essay examines the main dangers of artificial intelligence (AI) and digital entities, focusing on how they affect organizational control, governance, and regulation. It highlights accountability and enforcement gaps while discussing contemporary institutional responses, including the European Union's AI Act and other global initiatives. The ramifications for businesses are specifically discussed, with a focus on internal auditing, compliance, and risk management. The paper concludes by suggesting a framework for governance and policy that combines societal education, technological protections, and regulatory oversight. The main contention is that effective and innovative AI integration necessitates strong systems of accountability, transparency, and ethical oversight.

These challenges, the integration of AI in social sciences offers unprecedented opportunities for advancing knowledge and informing evidence-based policymaking. Interdisciplinary collaboration and ethical oversight are essential for harnessing the full potential of AI in social science.

This paper responds to Ron Horgan’s Science, Consciousness, Belief, and Survival (2025) and Crichton E. M. Miller’s rejoinder, addressing the question of whether Artificial Intelligence (AI) can possess consciousness comparable to humans. The analysis establishes a comparative framework distinguishing divine creation, human beings, and artificial artefacts. It argues that while human consciousness is authentic, dynamic, and rooted in the mystery of the soul, AI remains a derivative simulation—powerful in computation but devoid of true awareness or freedom. The paper emphasizes the theological and philosophical necessity of preserving the boundaries between Creator, human, and machine.

Sycophancy in neural networks-defined as the tendency of advanced models, particularly Large Language Models (LLMs), to excessively agree with or flatter users-has surfaced as a significant threat to the reliability, objectivity, and ethical deployment of artificial intelligence. This paper presents a comprehensive, formal investigation into neural network sycophancy, introducing and rigorously defining deferential biases as a core taxonomic class of AI bias. Drawing on the latest literature, empirical benchmarks, mechanistic analyses, and real-world case studies, we develop a novel multi-faceted framework for analyzing and mitigating deferential biases in neural networks. The paper carefully differentiates forms of sycophancy, reviews cutting-edge measurement strategies and mitigation techniques, and demonstrates both the theoretical and practical impacts of unchecked deferential bias on trust, factual accuracy, and safety in AI. Our findings emphasize the crucial necessity of integrating ethical, epistemic, and alignment-based approaches to develop more robust, trustworthy, and socially beneficial AI systems.

The traditional, age-based educational model, a relic of the industrial era, is increasingly misaligned with the needs of a rapidly evolving, technology-driven society. This paper proposes the "SchoolGamed" initiative, a new educational framework designed to replace standardized classes and curricula with a deeply personalized, adaptive, and engaging learning ecosystem. The model leverages three core technological and pedagogical pillars: 1) **Artificial Intelligence (AI)** to create individually tailored learning paths, 2) **Mixed Reality (MR)** to provide immersive, context-rich learning environments, and 3) **Gamification** principles to structure the curriculum as a series of achievement-based levels, allowing students to progress based on mastery rather than age. By integrating established theories of digital game-based learning, constructivism, and adaptive education, the SchoolGamed initiative aims to foster intrinsic motivation, enhance knowledge retention, and develop critical skills such as problem-solving, creativity, and collaboration. This research synthesizes findings from educational technology, cognitive psychology, and human-computer interaction to build the theoretical and practical case for this transformative approach to education, outlining its potential to create more equitable, effective, and engaging learning experiences for all students.

Bu çalışma, yapay zekâ üretimi içeriklerde görülen sistematik yanlılığı incelemekte ve bu yanlılıkların mizahi manipülasyon süreçlerine nasıl dönüşebileceğini analiz etmektedir. Araştırma, Donald Trump'ın 2025 yılında viral olan yapay zekâ üretimi Gazze videosu üzerinden yapay zekâ yanlılığı, politik iletişim ve mizah arasındaki kesişimi incelemektedir. Yapılan araştırmalar göstermektedir ki yapay zekâ modelleri, eğitildikleri veri setlerindeki toplumsal önyargıları öğrenebilir ve pekiştirebilir. Bununla birlikte yapılan tahminlere göre, 2025 yılına kadar internet içeriğinin %90'ının yapay zekâ tarafından üretileceği öngörülmektedir. Çalışmada TIBET metodolojisi kullanılarak Trump'ın videosu kapsamlı önyargı analizi açısından incelenmiştir. Bulgular, yapay zekânın komut (prompt) bağımlılığı nedeniyle sistematik önyargı üretiminin temel mekanizmasını oluşturduğunu göstermektedir. Veri setlerinin sömürgeci mirası, "palmiye ağaçları altında mutlu çocuk" gibi yönergeler verildiğinde sistemin Batılı kaynaklardan edindiği "egzotik yoksulluk" imgelerine yönelmesine neden olmaktadır. Araştırma, yapay zekanın "Filistinli mutlu bir çocuk" gibi yönergelere yanıt üretme kapasitesinin sınırlı olduğunu ortaya koymaktadır. Yapay zekâ üretimi içeriğin en tehlikeli yanı, mizah ile ciddi politik mesajları birleştirerek izleyicinin eleştirel düşünme yetisini sınırlandırmasıdır. Bu mizahi normalleştirme mekanizması, etnik temizlik gibi ağır politik önermeleri görsel eğlenceye dönüştürerek toplumsal gerçekliği çarpıtmaktadır. Sonuçlar, yapay zekâ teknolojilerinin demokratik söylemi tehdit edebilecek boyutta ideolojik manipülasyon araçlarına dönüşebileceğini göstermektedir.

This paper explores the impact of digital transformation and artificial intelligence (AI) on the Russian language in the context of contemporary geopolitics, educational decline, and social media influence. It offers a critical perspective on the current limitations of AI tools in accurately interpreting complex linguistic and literary problems, particularly in the realm of Russian literature. Using a detailed case study involving Tolstoy's story "Хозяин и Работник" and the concept of the "маленький человек", the paper discusses the inadequacies of AI-generated responses and the need for scholarly intervention in AI language training.

Jasmine Tinuoye | Liberation News

Silicon Valley isn't wrestling with its conscience. It's commodifying its guilt. While Big Tech props up "responsible AI" boards and posts DEI statements after harm is already done, Christian Ortiz did something no one else has. He solved the AI bias problem. That's not hyperbole. It's a historic breakthrough, and the fact that the world isn't already writing it into the canon tells you everything about the colonial gatekeeping still alive in tech. Ortiz, also known as ZacaTechO, is the Afro-Indigenous technologist behind Justice AI GPT and the Decolonial Intelligence Algorithmic Framework, or DIA. It's the first operational system that doesn't just identify bias in artificial intelligence. It eliminates the logic that creates it. "AI doesn't need to be more ethical. It needs to be decolonized," Ortiz says. Unlike those working inside the machine trying to make it fairer, Ortiz stepped outside its logic entirely and rebuilt it. Justice A.I. GPT was constructed from ancestral knowledge, systems abolition, and the lived realities of those most harmed by technological systems. He didn't come through elite labs. He didn't build for capital. He built for liberation.

This research paper explores the multifaceted impact of legal aid clinics in India, emphasizing their psychological and social contributions through the lens of placebo and nocebo effects. Legal aid clinics provide not only legal assistance but also psychological assurance, reducing stress and enhancing well-being among marginalized communities, akin to a placebo effect. Conversely, negative expectations can trigger a nocebo effect, undermining client satisfaction and service effectiveness. The paper examines the constitutional and statutory frameworks, including Articles 14, 21, and 39A of the Indian Constitution and the Legal Services Authorities Act, 1987, which underpin legal aid initiatives. It also highlights the role of clinical legal education (CLE) in fostering social justice and the challenges faced by legal aid clinics, such as resource constraints and lack of awareness. By integrating innovative approaches like the DISHA scheme and Lok Adalats, legal aid clinics bridge the justice gap, but systemic barriers necessitate ongoing reforms. This study underscores the need for positive communication strategies, enhanced funding, and technological integration to maximize the efficacy of legal aid services in promoting equitable access to justice.

Women Protection Cells (WPCs) in India, established to combat gender-based violence, have evolved since their inception in 1984 to provide critical support to women facing domestic violence and related crimes. These specialized units within police stations offer counselling, legal assistance, and community engagement to create a gender-sensitive environment for reporting crimes. While WPCs have increased reporting rates and improved police responsiveness, their effectiveness varies across regions due to systemic challenges such as inadequate funding, insufficient staffing, and patriarchal attitudes within law enforcement and society. Judicial developments, including the Protection of Women from Domestic Violence Act (PWDVA) of 2005, have strengthened their legal framework, but implementation gaps and cultural barriers persist. This paper examines the historical development, operational structure, regional variations, and impact of WPCs, highlighting their role in empowering women and the need for technological integration, legal reforms, and societal changes to enhance their effectiveness.

The advancements in artificial intelligence (AI) and the emergence of intelligent and superintelligent machines have significantly blurred the traditional boundaries between humans and machines. These technological developments have raised critical philosophical and ethical questions about the possibility of attributing moral agency to these systems. Can intelligent machines act morally, or should moral responsibility for their actions remain solely with their designers and operators? This paper aims to analyze these questions through a conceptual and analytical lens, focusing on the meaning, criteria, and philosophical implications of moral agency in the context of intelligent machines.

Generative Artificial Intelligence (AI) models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), have revolutionized data synthesis across various domains, including healthcare, finance, and entertainment. However, the rapid adoption of these technologies has raised significant concerns regarding data security and ethical misuse. One of the primary challenges is the potential for data leakage, where private information from training datasets may unintentionally be exposed through generated outputs. Additionally, the misuse of synthetic data for malicious purposes poses ethical dilemmas. This paper explores the development of secure generative AI models that integrate robust security mechanisms, privacy-preserving techniques, and ethical safeguards. We examine existing solutions for preventing data leakage and mitigating ethical concerns, highlighting the role of methods like differential privacy, adversarial training, and transparency frameworks. Furthermore, we propose strategies for advancing secure generative AI models and discuss the importance of creating standards for their ethical use. By addressing both technical and ethical challenges, this paper aims to contribute to the responsible deployment of generative AI technologies, ensuring they can be safely and ethically utilized in real-world applications.

As generative AI reshapes global societies, the Caribbean faces urgent questions about its legal readiness to regulate bias and discrimination embedded in these technologies. This article explores the risks posed by AI-driven bias, drawing on global examples and recent controversies, and evaluates whether current legal and data protection frameworks in the Caribbean are fit for purpose. It examines legal gaps in transparency, accountability, liability, and algorithmic fairness, and offers regulatory strategies tailored to regional realities. Drawing from international models such as the EU AI Act and UK regulatory guidance, the article proposes a rights-based, proactive approach for Caribbean policymakers to ensure ethical and equitable use of generative AI

This systematic review examines how ChatGPT is integrated into scientific research, assessing its contributions to hypothesis generation, academic writing, data analysis, and educational support across various fields. Utilising thirteen peer-reviewed and editorial sources from 2023 to 2024, the study evaluates the advantages and challenges of ChatGPT in driving scientific innovation. Although ChatGPT improves research efficiency and accessibility, concerns about authorship ethics, misinformation, bias, and transparency remain. The review highlights a disciplinary divide in the use of ChatGPT-varying from enthusiastic application in materials science to cautious approaches in public health and policy. It concludes by underscoring the necessity for ethical frameworks, methodological rigor, and interdisciplinary guidelines to promote the responsible integration of AI in scientific processes.

AI technologies have rapidly advanced, bringing transformative changes to various industries and aspects of daily life. However, the rise of AI has also raised significant ethical concerns regarding fairness, transparency, accountability, and privacy. This chapter delves into the development of responsible and transparent machine learning models, emphasizing the importance of ethical considerations in real-world applications.

In today's digital era, artificial intelligence (AI) has transformed the hiring process, with many organizations adopting AI-driven interviews to screen candidates. Proponents argue that these systems bring efficiency, scalability, and consistency to recruitment, particularly for companies sifting through

Objective: Considering the increasing spread of artificial intelligence and its algorithm-oriented nature and the increasing interaction of users with these algorithms in various platforms such as social networks, online stores, and search engines, the present study intends to examine the concept and necessity of algorithmic literacy and, in the following, the role of librarians who are actually specialists in information science in the education and development of this literacy. This research aims to study the concept and necessity of algorithmic literacy and the role of librarians in teaching this literacy.
Methodology: This research has been done in a narrative review method. In this way, related articles were extracted by searching the term algorithmic literacy in domestic and foreign reliable scientific databases. Then, this concept was examined and compared in terms of definition, dimensions, components, and other aspects, and consensus, disagreement, and research gaps were revealed.
Findings: The review of the literature revealed that despite the fact that researches on algorithmic literacy have been conducted abroad in recent years, this concept has not yet been studied in Iran. In this research, the development of the concept of literacy from the age of information to the age of artificial intelligence was investigated. Different definitions of algorithmic literacy were also mentioned. Next, the dimensions and components of algorithmic literacy and its importance and necessity were discussed. In the end, the role of librarians and libraries in teaching algorithmic literacy was discussed.
Conclusion: A look at the definition of literacy from the past to the present shows that the concept of literacy has always been changing due to technological changes. At first, literacy is defined as "the ability to read and write". Then, with the emergence of digital technologies, it is defined as "ability and awareness"; Finally, with technological advances, it is defined as "awareness and critical evaluation skills". All digital technologies are algorithmic machines and depend on algorithms to function. Now that we are in the era of artificial intelligence and artificial intelligence systems are built on the basis of algorithms, the need for literacy in relation to algorithms is felt more and more under the title of "algorithmic literacy". By reviewing the researches, it is clear that there is still no single and universal definition of algorithmic literacy, but by summarizing these researches, we can express this definition: "Awareness and recognition of algorithmic systems along with the ability and skill of critical evaluation of these systems". Also, there is no consensus on the dimensions and components of algorithmic literacy, and researchers have examined different dimensions, which overlapped in some cases. What emerges from the research review is that algorithmic literacy is related to the cognitive, behavioral and emotional dimensions of the user. If in the age of information, human interaction with information was investigated, today, in the era of artificial intelligence and algorithms, human interaction with algorithms should be investigated. Since in the past, libraries played an important role in teaching information literacy and librarians were its pioneers, now librarians and information science specialists can play a role as algorithmic literacy educators, according to their inherent duty.

Artificial intelligence (AI) models are widely adopted in various industries, yet their decision-making processes often exhibit biases that reflect societal inequalities. This review investigates how biases emerge in AI systems, the consequences of biased decision-making, and strategies to mitigate these effects. The paper follows a systematic review methodology, utilizing PRISMA guidelines to analyze existing literature. Key themes include data-driven biases, algorithmic influences, and ethical considerations in AI deployment. The review concludes with future research directions, emphasizing the need for fairness-aware AI models, robust governance, and interdisciplinary approaches to bias mitigation.

The unpreceded abilities of AI technologies have led to the emergence of new ethical issues; among them is the possibility of the moral agency of AI artifacts. There are many questions around this subject, including what are the necessary and sufficient conditions of being a moral agent? How can we examine those conditions in artifacts? What levels and degrees of agency are possible for artifacts? And what level of moral agency is proper for allocating a certain task to AI artifacts? There are wide discussions about factors that figure in the moral capacities of AI artifacts, and emotions are one of the frequently referred factors. Emotions are directly or indirectly relevant for examining AI’s moral status. In this paper, we focus on the relation between emotions and moral capacities in AI technologies. Our main question is whether emotions play a positive role in the improvement of moral capacities or a negative role. We extract and articulate four arguments in defense of the positive role of emotions in the enhancement of AI moral capacities, including arguments from moral sensitivity, bounded rationality, risk assessment, and culpability. Then, we present other four arguments in defense of the negative role of emotions in moral capacities including arguments from emotional hijacking, deceptive emotions, anthropomorphism and dehumanization paradox, and moral deskilling. Finally, we analyze the debate by clarifying the point of contest between the two mentioned camps and discuss serious challenges of designing emotional AI.

The rapid advancement of artificial intelligence has transformed many aspects of modern society, while heightening concerns about existential threats and unforeseen consequences. Researchers have systematically evaluated AI failures and hypothetical catastrophic predictions throughout this study, emphasizing superintelligence control problems combined with autonomous weapons, economic destruction of society, cyber warfare threats, and artificial intelligence-driven biotechnology dangers that affect the climate. This research investigates ethical, legal, and security concerns concerning AI autonomy through analyses of AI governance frameworks, empirical case studies, and policy reports. The research reveals that unrestricted AI deployment presents the risk of destructive situations, encompassing the combined effects of economic turmoil, personnel job loss, military operation supervision removal, elevated cyber hazards, environmental disturbances, and synthetic biology complications. The resolution of these challenges depends on strengthened lawmaking and ethical governance of AI and international partnerships. The research confirms the immediate need to protect against AI dangers before full AI potential can be attained for human development.

The moral, ethical, and legal protections of artificial intelligence (AI) have come under scrutiny due to recent developments in the field. A more ethical approach to managing AI technology is urgently required, as is the development of better measures for evaluating AI system privacy and security. To tackle these issues, we suggest a model for AI maturity and a framework for AI trust to improve confidence in AI system design and administration. For AI to be trusted, people and machines must first reach a mutual understanding on the system's performance. Improved openness and confidence in unregulated "black box" AI systems are goals of the framework's "entropy lens" research, which is based on information theory. In highly competitive and unpredictable settings, human trust in AI systems can be diminished due to their high entropy. This study uses insights from entropy research to enhance the reliability and efficiency of autonomous human-machine teams and systems, particularly those including hierarchical components and their interconnections. Using this perspective to boost faith in AI also reveals untapped potential for team efficiency. We provide two examples to show that the AI framework can accurately gauge confidence in AI system design and administration. For its outstanding capacity to produce realistic data, Generative Artificial Intelligence (GAI) has set off a revolutionary wave in many fields, such as machine learning, healthcare, commerce, and the entertainment industry. An exhaustive analysis of the privacy and security issues related to GAI is provided by this survey. It offers five crucial viewpoints that are necessary for a thorough comprehension of these complexities. Various generative model types, GAI designs, practical applications, and current advances in the field are covered in the study. It also notes existing security methods and suggests long-term fixes with an emphasis on participation from users, developers, institutions, and lawmakers.

The moral, ethical, and legal protections of artificial intelligence (AI) have come under scrutiny due to recent developments in the field. A more ethical approach to managing AI technology is urgently required, as is the development of better measures for evaluating AI system privacy and security. To tackle these issues, we suggest a model for AI maturity and a framework for AI trust to improve confidence in AI system design and administration. For AI to be trusted, people and machines must first reach a mutual understanding on the system's performance. Improved openness and confidence in unregulated "black box" AI systems are goals of the framework's "entropy lens" research, which is based on information theory. In highly competitive and unpredictable settings, human trust in AI systems can be diminished due to their high entropy. This study uses insights from entropy research to enhance the reliability and efficiency of autonomous human-machine teams and systems, particularly those including hierarchical components and their interconnections. Using this perspective to boost faith in AI also reveals untapped potential for team efficiency. We provide two examples to show that the AI framework can accurately gauge confidence in AI system design and administration. For its outstanding capacity to produce realistic data, Generative Artificial Intelligence (GAI) has set off a revolutionary wave in many fields, such as machine learning, healthcare, commerce, and the entertainment industry. An exhaustive analysis of the privacy and security issues related to GAI is provided by this survey. It offers five crucial viewpoints that are necessary for a thorough comprehension of these complexities. Various generative model types, GAI designs, practical applications, and current advances in the field are covered in the study. It also notes existing security methods and suggests long-term fixes with an emphasis on participation from users, developers, institutions, and lawmakers.

This study investigates how contemporary artificial intelligence image generation systems interpret and reproduce Indian cultural elements through a comparative analysis of three major platforms: Stable Diffusion, Flux, and Midjourney. While these systems have demonstrated remarkable technical capabilities, their handling of non-Western cultural elements remains understudied. We present a novel methodological framework that combines visual social semiotics with digital anthropology to analyse AI-generated images across multiple dimensions, including representational accuracy, cultural sensitivity, and power dynamics. Our systematic analysis of images generated through increasingly sophisticated prompts reveals significant patterns in how these systems mediate cultural representation. Results indicate that while platforms exhibit varying technical proficiencies, they consistently demonstrate concerning biases in human representation, particularly in their treatment of gender, class, and ethnic identities. The analysis reveals systematic simplification of complex cultural elements and the persistence of orientalist perspectives, despite advances in technical capabilities. These findings suggest that improved technical sophistication alone is insufficient for authentic cultural reproduction; rather, fundamental reconsideration of how these systems process and understand cultural information is necessary. This research contributes both theoretical insights into digital cultural representation and practical implications for developing more culturally sensitive AI systems, while highlighting crucial areas for improvement in the technical architecture of image generation models.

Platforms have emerged as a new kind of regulatory object over a short period of time. There is accelerating global regulatory competition to conceptualise and govern online platforms in response to social, economic and political discontentarticulated in terms such as 'fake news', 'online harms' or 'dark patterns'. In this paper, we empirically map the emergence of the regulatory field of platform regulation in the UK. We focus on the 18-month period between September 2018 and February 2020 which saw an upsurge of regulatory activism. Through a legal-empirical content analysis of eight official reports issued by the UK government, parliamentary committees and regulatory agencies, we (1) code over 80 distinct online harms to which regulation is being asked to respond; we (2) identify eight areas of law referred in the reports (data protection and privacy, competition, education, media and broadcasting, consumer protection, tax law and financial regulation, intellectual property law, security law); we (3) analyse nine agencies mentioned in the reports for their statutory and accountability status in law, and identify their centrality in the regulatory network; we (4) assess their regulatory powers (advisory, investigatory, enforcement); and the regulatory toolbox of potential measures ascribed to agencies; we (5) quantify the number of mentions platform companies received in the reports analysed. We find that Ofcom (the communications regulator) and the CMA (the Competition and Markets Authority) are the most central actors in the regulatory field, with the Information Commissioner (the data regulator) following close behind. We find that security-and terrorism-related interventions remain particularly obscure and hard to capture with a socio-legal analysis of public documents. We find that the political focus is overwhelmingly on a handful of US multinational companies. Just two companies, Google and Facebook, account for three-quarters of the references made to firms in the documents we examined. Six Chinese firms are mentioned, and two EU firms. Not a single UK-headquartered company appears. This could be interpreted as a focus on the defence of national sovereignty that has crowded out questions of market entry or innovation in the UK. We find that the regulatory agenda is driven by an ever-wider list of harms, with child protection, security and misinformation concerns surfacing in many different forms. We also identify an amorphous and deep disquiet with lawful but socially undesirable activities. We suggest that this 'moral panic' has engendered an epistemic blind spot regarding the processual questions that should be at the core of rule-governed regulation: how to monitor (by way of information-gathering powers), trigger intervention, and remove and prevent certain kinds of content. Filtering technologies, processes of notification, redress mechanisms, transparency and audit requirements all need to be addressed. The question arises as to whether the emergent recourse to codes of practice or codes of conduct (for example delegating content moderation functions to private firms) will be appropriate to address the wide range of regulatory challenges now faced. We delineate a further epistemic gap -the effects of platform regulation on cultural production and consumption. Platforms' roles as cultural gatekeepers in governing information flows (content identification, rankings, recommendations), and directing remuneration still remain poorly understood.

In compatibilists believe that we cannot act freely and be morally responsible for an action in a deterministic world, while compatibilists don’t deny the possibility of free action and moral responsibility for the agent, and they believe that we can find some kind of compatibility between moral responsibility and causal determination. This article will analyze Alfred Mele's zygote argument of manipulation, which is an argument in critique of compatibilism between moral responsibility and the deterministic world. Manipulation argument is an argument based on an agent's intuition of a situation in which the agent is under the control of another person or force. It wants to show that an agent in a manipulated situation, despite satisfying the compatibilists conditions, is not morally responsible for her action, and she is like the agent in a deterministic world.

In compatibilists believe that we cannot act freely and be morally responsible for an action in a deterministic world, while compatibilists don’t deny the possibility of free action and moral responsibility for the agent, and they believe that we can find some kind of compatibility between moral responsibility and causal determination. This article will analyze Alfred Mele's zygote argument of manipulation, which is an argument in critique of compatibilism between moral responsibility and the deterministic world. Manipulation argument is an argument based on an agent's intuition of a situation in which the agent is under the control of another person or force. It wants to show that an agent in a manipulated situation, despite satisfying the compatibilists conditions, is not morally responsible for her action, and she is like the agent in a deterministic world.

Artificial intelligence (AI) hallucinations, characterized by generating inaccurate or non-factual content by AI systems, pose significant risks across various domains. This thesis investigates the phenomenon of AI hallucinations in large language models (LLMs), exploring their technical roots, ethical implications, and societal consequences. The work introduces an interdisciplinary framework for understanding hallucinations, incorporating computer science, cognitive psychology, and regulatory ethics perspectives. Using case studies from healthcare, legal systems, and content moderation, this thesis provides actionable strategies for detecting, mitigating, and preventing hallucinations. It concludes with a proposal for hallucination-resilient AI architectures and a roadmap for policymakers, developers, and organizations deploying AI systems.

This study identifies alternative models for the production of AI-generated images to those currently used by mainstream AI platforms. Based on primitive computational art processes, these systems allow designers to gain greater control over the final visual result, while avoiding potential issues with intellectual property theft and breach of privacy. The article starts by analysing the level of artificiality that might be effectively attributed to each part of the creative process involved in the development of AI-generated images. It then moves on to discuss the extent to which individual actors intervene in each of these stages, with the aim of untangling the different subjectivities that would ultimately shape the meaning of AI-generated photographs, and identifying existing AI models able to offer practical solutions for every designer to retain creative autonomy over their AI creations.

هدف از این پژوهش نقد به رویکردی است که راهکار برطرف شدن چالش‌هایِ اخلاقیِ هوشِ مصنوعیِ را محدود به طراحی و اصلاحات فنی می‌داند. برخی پژوهش‌گران چالش‌های اخلاقی در هوش مصنوعی را همگرا تلقی می‌کنند و معتقدند این چالش‌ها همانطور که با ظهور سیستم هوش مصنوعی پدید آمدند، با پیشرفت و اصلاحات فنی آن مرتفع خواهند شد. در مباحثِ اخلاقِ هوش مصنوعی، موضوعاتی همچون حفاظت از حریم خصوصی و شفافیت در بیشتر پژوهش‏ها مورد توجه قرار گرفته است. در پژوهش حاضر با استفاده از روشِ تحلیلی-انتقادی، همگرایی میان شفافیت و حریم خصوصی در سیستم‌های یادگیری ماشین بررسی شده و رویکردی که رفع چالش‌های اخلاقی را به طراحی هوش مصنوعی محدود می‌داند به نقد گذاشته شده است. نتایج این بررسی نشان می‏دهد که همگرایی میان چالش‌های اخلاقی هوش مصنوعی وجود ندارد. همچنین با مطرح کردن سه چالش کمی‏سازی، محدودیت‏های تکنیکی و مباحث فرااخلاقی به نقد رویکردهایی که تنها بر طراحی و اصلاحات فنی متکی هستند پرداخته شد. نتایج این بخش نشان می‌دهد که نمی‌توان تنها با تکیه بر طراحی، به چالش‌های اخلاقی در هوش مصنوعی پاسخ داد و نیاز به استفاده از سایر روش‌ها مانند قانون‌گذاری و یا پیشرفت در سایر علوم در کنار توجه به طراحی وجود دارد.
This research aims to criticize the approach that considers the solution to the ethical challenges of artificial intelligence (AI) to be only in design and technical improvements. Some researchers consider the ethical challenges in AI to be convergent, which emerge with the advent of AI systems and are resolved with technical progress and improvements. In the discussions of the ethics of AI, issues such as privacy and transparency have been the focus of most studies. In the present research, using the analytical-critical method, the convergence between transparency and privacy in machine learning systems was investigated, and the approach that limits the resolution of ethical challenges to AI design was criticized. The research findings indicate that there is no convergence between the ethical challenges of AI. Additionally, by raising three challenges of quantification, technical limitations, and meta-ethical issues, the criticism of approaches that rely solely on design and technical improvements was addressed. The results also indicate that it is not possible to respond to the ethical challenges of AI only by relying on design, and there is a need to use other methods such as legislation or progress in other sciences in addition to attention to design.

Artificial intelligence (AI) and machine learning (ML) are transforming higher education by enhancing personalized learning and academic support, yet they pose significant ethical challenges, particularly in terms of inherent biases. This review critically examines the integration of AI in higher education, underscoring the dual aspects of its potential to innovate educational paradigms and the essential need to address ethical implications to avoid perpetuating existing inequalities. The researchers employed a methodological approach that analyzed case studies and literature as primary data collection methods, focusing on strategies to mitigate biases through technical solutions, diverse datasets, and strict adherence to ethical guidelines. Their findings indicate that establishing an ethical AI environment in higher education is imperative and involves comprehensive efforts across policy regulation, governance, and education. The study emphasizes the significance of interdisciplinary collaboration in addressing the complexities of AI bias, highlighting how policy, regulation, governance, and education play pivotal roles in creating an ethical AI framework. Ultimately, the paper advocates for continuous vigilance and proactive strategies to ensure that AI contributes positively to educational settings, stressing the need for robust frameworks that integrate ethical considerations throughout the lifecycle of AI systems to ensure their responsible and equitable use.

The rapid advancement of artificial intelligence (AI) technologies has fundamentally transformed the landscape of information technology (IT), offering unprecedented opportunities for innovation and efficiency. However, these advancements also bring significant ethical challenges, including issues of bias, privacy, transparency, and accountability. This paper explores these ethical challenges and proposes a comprehensive ethical framework for the responsible development and deployment of AI in IT. Through an examination of historical context, current trends, and detailed case studies, the framework aims to provide actionable guidelines to mitigate biases, protect privacy, enhance transparency, and ensure accountability in AI systems. By fostering ethical AI practices, this framework aspires to support the sustainable and equitable advancement of AI technologies, ultimately benefiting society as a whole.

Black boxes in machine learning (ML) systems can be understood in at least two ways; in relation to (1) an algorithm, i.e., a decision rule, when that rule is impossible for a human to interpret, or (2) the secrecy of that rule (proprietary nature of it), due to business or economic factors. I call the first understanding "algorithmic black boxes" and the second "institutional black boxes". These two understandings are independent of each other, in particular, transparent algorithms can be part of systems that are institutional black boxes. I indicate that when it comes to the application of ML in public institutions applying the law (e.g., courts), institutional black boxes pose a particular threat to the integrity and reliability of ML systems used in such a context. I argue that in the eXplainable Artificial Intelligence trend, more attention should be paid not only to the favourable features of the algorithm (e.g., direct interpretability) but also to the business context in which the ML system is developed. Its secrecy can sabotage the transparency of even the simplest models.

The purpose of this paper is to introduce and present the context, theoretical frameworks, and methodological approach of an educational design research study that seeks to better understand pedagogical, social, and technological design characteristics to foster a learner-centered, inclusive online learning environment. This research is grounded in feminist technoscience and postphenomenology. These theoretical frameworks guide the research question and methodological approach. A purposeful sampling of educators will inform the initial analysis and exploration phases of research, followed by an iterative process of design, evaluation, and enhancement phases. The research process will conclude with a reflection phase and identification of design principles.

Dans cet article, nous menons une réflexion sur l’Intelligence Artificielle au prisme d’une approche intersectionnelle qui questionne l’apparente neutralité de cette Technique et met en visibilité les différents impacts de cette dernière sur les rapports sociaux. Nous proposons d’analyser le principe de justice dans la gouvernance de l’IA au travers de l’exploration et de l’étude de trois chartes développées entre 2020 et 2021, en France. Il s’agira de présenter les points de convergence, de divergence et les limites de ces instruments, ainsi que leur inscription dans un contexte plus large de production de recommandations traitant de l’éthique et de la justice de l’IA.

This analysis uses a postphenomenological lens to provide insight into the shift occurring within society at large. It focuses on the educational domain, and arguing for a reevaluation of instructive approaches. Philosophical research into technology and education is seemingly lacking, and so this article seeks to fill the present gaps. This analysis initially delves into the postphenomenological frameworks of technological mediation, intentionality, and dimensions, to clearly differentiate the embodiment and cybernetic relationships as they are understood within various texts. Following this the epistemic and practical dimensions of these relations are explored to then be juxtaposed with the descriptive argument of the overall cultivating cybernetic relationship between human user and technological artefact in contemporary times, using the smartphone as the core example case study. Finally, a normative argument is made considering the previous cybernetic insight, in that if the classroom setting is to evolve and adapt, it too must embed technology within the classroom, lessons, and overall educational engagement. It represents an equalizing technological balance of cybernetic student and immersive classroom where the intentionality of user, technology, and classroom blend together to continue the cultivation of our blossoming relationship with technology.This analysis uses a postphenomenological lens to provide insight into the shift occurring within society at large. It focuses on the educational domain, and arguing for a reevaluation of instructive approaches. Philosophical research into technology and education is seemingly lacking, and so this article seeks to fill the present gaps. This analysis initially delves into the postphenomenological frameworks of technological mediation, intentionality, and dimensions, to clearly differentiate the embodiment and cybernetic relationships as they are understood within various texts. Following this the epistemic and practical dimensions of these relations are explored to then be juxtaposed with the descriptive argument of the overall cultivating cybernetic relationship between human user and technological artefact in contemporary times, using the smartphone as the core example case study. Finally, a normative argument is made considering the previous cybernetic insight, in that if the classroom setting is to evolve and adapt, it too must embed technology within the classroom, lessons, and overall educational engagement. It represents an equalizing technological balance of cybernetic student and immersive classroom where the intentionality of user, technology, and classroom blend together to continue the cultivation of our blossoming relationship with technology.

This paper reviews the deployment of Large Language Models (LLMs), particularly OpenAI's ChatGPT-4, within critical sectors such as finance, education, and healthcare. It discusses the potential risks associated with the use of LLMs, including misinformation, biases, and the inadvertent breach of privacy. Furthermore, it highlights the responses from various regulatory bodies and the measures suggested to mitigate these risks. The study underscores the need for robust regulatory frameworks to ensure transparency, accountability, and the ethical use of AI technologies.

This paper argues that Machine Learning (ML) algorithms must be educated. ML-trained algorithms' moral decisions are ubiquitous in human society. Sometimes reverting the societal advances governments, NGOs and civil society have achieved with great effort in the last decades or are yet on the path to be achieved. While their decisions have an incommensurable impact on human societies, these algorithms are within the least educated agents known (data incomplete, un-inclusive, or biased). ML algorithms are not something separate from our human idiosyncrasy but an enactment of our most implicit prejudices and biases. Some research is devoted to "responsibility assignment" as a strategy to tackle immoral AI behaviour. Yet this paper argues that the solution for AI ethical decision-making resides in algorithm education" (as opposed to the "training") of ML. Drawing from an analogy between ML and child education for social responsibility, the paper offers clear directions for responsible and sustainable AI design, specifically with respect to how to educate algorithms to decide ethically.

The rapid rise of the ride-hailing sector is opening new avenues for women to participate in the Indian platform economy. However, women continue to be underrepresented as transport providers as much of the debate in the ride-hailing sector centers around women as transport users, not as transport providers. In this paper, we critically explore the strategies to enhance the participation of women drivers in the ride-hailing sector by conducting in-depth qualitative interviews with the stakeholders such as women taxi drivers, platform providers, NGOs, and policy and research support groups in India. The interviews are supported by analyzing documents related to government policies, platform companies, organizations, and the policies mentioned by the participants. This study builds on the Feminist HCI (Human-Computer Interaction) theory that proposes technologies should integrate feminist values in the design (Bardzell, 2010). The paper advocates for redesigning the ride-hailing platforms in alignment with marginalized actors.