Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on the classic (...) counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients’ health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted. (shrink)

This article presents a mapping review of the literature concerning the ethics of artificial intelligence (AI) in health care. The goal of this review is to summarise current debates and identify open questions for future research. Five literature databases were searched (Scopus, Google Scholar, Philpapers, Web of Science, Pub Med), in April 2019, to support the following research question: “how can the primary ethical risks presented by AI-health be categorised, and what issues must policymakers, regulators and developers consider in order (...) to be ‘ethically mindful?’”. A series of screening stages were carried out—for example, removing articles that focused on digital health in general (e.g. data sharing, data access, data privacy, surveillance/nudging, consent, ownership of health data, evidence of efficacy)—yielding a total of 156 papers that were included in the review.We find that ethical issues can be (a) epistemic, related to misguided, inconclusive or inscrutable evidence; (b) normative, related to unfair outcomes and transformative effectives; or (c) related to traceability. We further find that these ethical issues arise at six levels of abstraction: individual, interpersonal, group, institutional, and societal or sectoral. Finally, we outline a number of considerations for policymakers and regulators, mapping these to existing literature, and categorising each as epistemic, normative or traceability-related and at the relevant level of abstraction. This article contributes to the debate on AI in health care by offering a comprehensive analysis of the relevant literature, focusing on the ethical implications for individuals, interpersonal relationships, groups, institutions, societies and the health sector as a whole. Our goal is to inform policymakers, regulators and developers of what they must consider if they are to enable health and care systems to capitalise on the dual advantage of ethical AI; maximising the opportunities to cut costs, improve care, and improve the efficiency of health and care systems, whilst proactively avoiding the potential harms. We argue that if action is not swiftly taken in this regard, a new ‘AI winter’ could occur due to chilling effects related to a loss of public trust in the benefits of AI for health care. (shrink)

Political protests, debates on college campuses, and social media tirades make it seem like everyone is speaking their minds today. Surveys, however, reveal that many people increasingly feel like they're walking on eggshells when communicating in public. Speaking your mind can risk relationships and professional opportunities. It can alienate friends and anger colleagues. Isn't it smarter to just put your head down and keep quiet about controversial topics? In this book, Hrishikesh Joshi offers a novel defense of speaking your (...) mind. He explains that because we are social creatures, we never truly think alone. What we know depends on what our community knows. And by bringing our unique perspectives to bear upon public discourse, we enhance our collective ability to reach the truth on a variety of important matters. Speaking your mind is also important for your own sake. It is essential for developing your own thinking. And it's a core aspect of being intellectually courageous and independent. Joshi argues that such independence is a crucial part of a well-lived life. The book draws from Aristotle, John Stuart Mill, Friedrich Nietzsche, Bertrand Russell, and a range of contemporary thinkers to argue that it's OK to speak your mind. Key Features Shows that we have not just a right but a moral duty to publicly share what we know. Argues that discussing your unique ideas with others is essential for developing as a critical thinker. Explores the value of intellectual honesty and independence in the writings of John Stuart Mill and Friedrich Nietzsche and connects their thinking to contemporary problems. Argues that avoiding cultural blind spots today is important for the fate of future generations. (shrink)

In philosophical work on the ethics of conversational exchange, much has been written regarding the speaker side—i.e., on the rights and duties we have as speakers. This paper explores the relatively neglected topic of the duties pertaining to the listeners’ side of the exchange. Following W.K. Clifford, we argue that it's fruitful to think of our epistemic resources as common property. Furthermore, listeners have a key role in maintaining and improving these resources, perhaps a more important role than speakers. We (...) develop this idea by drawing from Hugo Mercier and Dan Sperber's “interactionist” picture of reason, which suggests that reasoning is essentially dialogical and relies on the epistemic vigilance of listeners. The paper defends an imperfect, prima facie duty to listen, one that is sufficiently strong to place substantial demands on individuals, but not so overly demanding as to be implausible. (shrink)

The American political landscape exhibits significant polarization. People’s political beliefs cluster around two main camps. However, many of the issues with respect to which these two camps disagree seem to be rationally orthogonal. This feature raises an epistemic challenge for the political partisan. If she is justified in consistently adopting the party line, it must be true that her side is reliable on the issues that are the subject of disagreements. It would then follow that the other side is anti-reliable (...) with respect to a host of orthogonal political issues. Yet, it is difficult to find a psychologically plausible explanation for why one side would get things reliably wrong with respect to a wide range of orthogonal issues. While this project’s empirical discussion focuses on the US context, the argument generalizes to any situation where political polarization exists on a sufficiently large number of orthogonal claims. (shrink)

This paper defends a provocative thesis: namely, that the present structure and composition of the academy undermines democratic legitimacy. Political philosophers have often stressed that universal suffrage by itself is not sufficient for such legitimacy. In these critiques, they have focused on the disproportionate power of the wealthy to shape politics and public discourse. I argue here that there is a deeper and relatively unnoticed problem in this vein: the university system exerts enormous power in shaping the perspectives of the (...) public, through a variety of means. Yet, the academy is democratically unaccountable for all practical purposes. Moreover, as has been documented in a range of empirical work, it is politically homogenous. As a result, I argue, this asymmetric power undermines the legitimacy of the political system, viewed as a whole. This can be seen from within three influential accounts of democratic legitimacy: republicanism, public reason liberalism, and consent theories. The paper concludes by exploring some potential remedies in the service of moving towards democratic legitimacy. (shrink)

This paper argues for the existence of a certain type of defeater for one’s belief that P—the presence of social incentives not to share evidence against P. Such pressure makes it relatively likely that there is unpossessed evidence that would provide defeaters for P because it makes it likely that the evidence we have is a lopsided subset. This offers, I suggest, a rational reconstruction of a core strand of argument in Mill’s On Liberty. A consequence of the argument is (...) that on morally and politically laden issues in particular, a high degree of doxastic openness might be appropriate. (shrink)

John Stuart Mill claims in On Liberty that the robust ability for individuals to question and dispute claims is a necessary condition for us to be justified in believing those claims. But it is not obvious why even bad objections must be allowed to be voiced. On a recent interpretation of Mill’s view, censorship of bad objections does not threaten experts’ justification, but it does threaten laypeople’s justification. Laypeople must be assured that experts are in the business of responding to (...) objections, rather than unfairly dismissing them. But laypeople cannot tell which objections are bad, so even these must be allowed to be voiced. Here, I argue that there is no way to cordon off experts from the epistemically deleterious effects of censorship, regarding many questions of interest. If Mill’s argument is right, then, the epistemic impacts of censorship are more radical than it might seem at first glance. (shrink)

This paper presents a comprehensive investigation into the role of prompt engineering in optimizing the effectiveness of large language models (LLMs) like ChatGPT-4 and Google Gemini for financial market integrity and risk management. As AI tools are increasingly integrated into financial services, including credit risk analysis, market risk evaluation, and financial modeling, prompt engineering has become crucial for improving the relevance, accuracy, and contextual alignment of AI-generated outputs. This study evaluates the impact of various prompt configurations in enhancing financial decision-making. (...) Through a series of experiments, the paper compares the performance of ChatGPT-4 and Google Gemini (versions 1.5 and 2.0) in generating actionable insights for credit and market risk analysis. The results reveal that ChatGPT-4 outperforms Google Gemini by over 30% in generating accurate financial insights. Additionally, ChatGPT-4 Version 4 is found to be 20% more effective than Version 3 in risk analysis tasks, particularly in aligning with regulatory frameworks and financial data. These improvements highlight the significant role of prompt engineering in enhancing the precision of financial models. Furthermore, the study explores the reduction of error rates through optimized prompt strategies. In particular, prompt engineering reduces error rates by approximately 20% when assessing complex financial queries. (shrink)

Epistemic paternalism involves interfering with the inquiry of others, without their consent, for their own epistemic good. Recently, such paternalism has been discussed as a method of getting the broader public to have more accurate views on important policy relevant matters. In this paper, I discuss a novel problem for such paternalism—what I call epistemic spillovers. The problem arises because what matters for rational belief is one’s total evidence, and further, individual pieces of evidence can have complex interactions with one (...) another. Because of this, I argue that justified epistemic paternalism requires the would-be paternalist to be in an unusually strong epistemic position, one that most would-be paternalists are unlikely to meet. (shrink)

According to many philosophers, the world should embrace open borders – that is, let people move around the globe and settle as they wish, with exceptions made only in very specific cases such as fugitives or terrorists. Defenders of open borders have adopted two major argumentative strategies. The first is to claim that immigration restrictions involve coercion, and then show that such coercion cannot be morally justified. The second is to argue that adopting worldwide open borders policies would make the (...) world a much better place, particularly by improving average well-being. This essay contends that both of these argumentative strategies fail. Some immigration restrictions are not only morally justified, but morally required. (shrink)

The rapid proliferation of Artificial Intelligence (AI) systems across diverse sectors—including healthcare, critical infrastructure, and digital experiences—has unveiled critical interoperability challenges that poses a challenge to the ongoing innovation, safety, equitable access, and the global competitiveness of U.S. AI technologies. This paper presents a comprehensive analysis of the current AI interoperability landscape, examining technical standards, regulatory frameworks, and governance models across major economic regions. By studying current developments we identify significant fragmentation in AI development ecosystems, with divergent approaches emerging between (...) the United States, European Union, China, and other key players, highlighting strategic implications and proposals for maintaining U.S. leadership. Our research examines technical interoperability challenges in data formats, model architectures, workflow orchestration, and multi-agent frameworks, while analyzing regulatory divergence in AI governance approaches, including the EU’s risk-based AI Act and the U.S.’s sectoral strategies. The cooperation for standardization AI protocols, data and models between various countries, organization, companies, domains and technologies have been discussed. We synthesize emerging standards and risk management methodologies from leading international bodies such as ISO/IEC JTC 1/SC 42, IEEE, and NIST, including ISO/IEC 42001 for AI management systems and the NIST AI RMF, and explore the role of model cards and data specifications in achieving technical interoperability. We also put forward looking scenarios for the next five years in this subject. By integrating insights from industry whitepapers, government publications, and academic research, we propose a holistic framework for global AI interoperability that addresses both technical standardization and regulatory harmonization keeping US AI landscape in mind. This framework provides policymakers, industry leaders, and standards organizations with actionable pathways to ensure AI systems are not only powerful, safe, and trustworthy but also strategically positioned to reinforce U.S. AI leadership while enabling seamless collaboration across borders and domains in alignment with regional regulatory requirements and cultural contexts. (shrink)

This paper presents a comprehensive review of Artificial General Intelligence (AGI) and Agentic AI, examining their technological foundations, current capabilities, and future trajectories. The study identifies key technical distinctions between these AI paradigms, including their architectural requirements, computational demands, and learning mechanisms. We survey the current technical landscape, including specialized frameworks like OpenAI’s AGI classification system and emerging Agentic AI platforms such as Vectara-agentic and CrewAI. The paper also examines the hardware infrastructure and cloud services enabling these advanced AI systems, (...) from NVIDIA’s specialized GPUs to large-scale projects like OpenAI’s proposed "Stargate" initiative and others. Our comparative analysis reveals that Agentic AI is rapidly transitioning from research to practical deployment across industries including legal services, DevOps, and enterprise automation, while AGI remains in the research phase with ongoing debates about its feasibility and timeline. The paper discusses critical challenges in both domains, including safety considerations, alignment problems, and governance requirements. We highlight how Agentic AI serves as a bridge between today’s generative AI capabilities and future AGI aspirations, offering autonomous functionality while avoiding some of AGI’s unresolved risks. (shrink)

Within the immigration debate, libertarians have typically come down in favor of open borders by defending two main ideas: i) individuals have a right to free movement; and ii) immigration restrictions are economically inefficient, so that lifting them can make everyone better off. This entry describes the rationale for open borders from a libertarian perspective (in part by analogy to the debate around minimum wage laws). Three main objections within the immigration literature are then discussed: i) the view that states (...) may restrict immigration to protect the interests (e.g. jobs) of their domestic needy; ii) the claim that restrictions are justified as a means of preserving culture; and iii) the influential argument that the right to freedom of association entails a right to exclude. These arguments are well-known, and I describe some of the compelling responses to be found in the recent literature. The essay then turns to more neglected critiques. The first concerns how liberal regimes should think about immigration from countries where the dominant norms are illiberal. The second examines the idea that political and economic institutions are sensitive to immigration policy, especially in the long run. Both of these critiques approach the topic from a non-ideal theory perspective, emphasizing the “public choice” dimensions of immigration policy. The entry closes by suggesting that this form of critique is especially important for open-borders libertarians to respond to given their general commitments to non-ideal, non-romantic theorizing in the context of government institutions and policy. (shrink)

The rapid evolution of artificial intelligence has given rise to agentic AI systems—autonomous entities capable of perceiving their environment, making decisions, and executing actions with minimal human intervention. This work provides a systematic analysis of agentic AI frameworks, governance models, and implementation strategies. Drawing on a comprehensive review of the literature, we examine the current state of agentic AI technologies, highlight key challenges in governance, security, and ethical oversight, and compare architectural frameworks for responsible deployment. Our results, illustrated through detailed (...) framework comparisons and governance analyses, demonstrate that while agentic AI holds transformative potential across multiple sectors, notable gaps persist in standardization, regulatory compliance, and interoperability. To address these issues, we propose a layered architecture that embeds governance and security across all system layers. An analysis of the competitive landscape further identifies critical interoperability challenges that could undermine U.S. leadership. Based on these insights, we outline a strategic framework for U.S. competitiveness, emphasizing accelerated standards development, international collaboration, and investment in interoperability research. Finally, emerging trends and future directions are explored to provide a comprehensive roadmap for responsible deployment of agentic AI. -/- . (shrink)

The rapid advancement of artificial intelligence (AI), particularly agentic AI systems capable of autonomous decision-making, has exposed significant gaps in existing federal regulatory frameworks. This paper examines the regulatory barriers inhibiting AI innovation and adoption identified in the Office of Science and Technology Policy's (OSTP) Request for Information (RFI) on regulatory reform. We analyze five categories of barriers—regulatory mismatches, structural incompatibility, lack of clarity, direct hindrance, and organizational factors—and propose a comprehensive governance framework integrating technical standards, risk management protocols, and (...) policy recommendations. Drawing from extensive literature on AI governance tools and frameworks, we present actionable solutions for modernizing federal regulations to foster responsible AI innovation while maintaining public trust and safety. (shrink)

Following Anthony Downs’s classic economic analysis of democracy, it has been widely noted that most voters lack the incentive to be well-informed. Recent empirical work, however, suggests further that political partisans can display selectively lazy or biased reasoning. Unfortunately, political knowledge seems to exacerbate, rather than mitigate, these tendencies. In this paper, I build on these observations to construct a more general skeptical challenge which affects what I call creedal beliefs. Such beliefs share three features: (i) the costs to the (...) individual of being wrong are negligible, (ii) the beliefs are subject to social scrutiny, and (iii) the evidential landscape relevant to the beliefs is sufficiently complex so as to make easy verification difficult. Some philosophers and social scientists have recently argued that under such conditions, beliefs are likely to play a signaling, as opposed to a navigational role, and that our ability to hold beliefs in this way is adaptive. However, if this is right, I argue there is at least a partial debunker for such beliefs. Moreover, this offers, I suggest, one way to develop the skeptical challenge based on etiological explanation that John Stuart Mill presents in On Liberty when he claims that the same causes which lead someone to be a devout Christian in London would have made them a Confucian in Peking. Finally, I contend that this skeptical challenge is appropriately circumscribed so that it does not over-extend in an implausible way. (shrink)

This work reviews U.S. workforce retention, AI upskilling, prompt engineering, workforce development, and automation in the context of recent advancements in agentic generative AI. The rapid integration of artificial intelligence (AI) across industries has raised concerns over potential job displacement within the US workforce. However, targeted upskilling-particularly through training in prompt engineering, a key skill for interacting with large language models-offers promising avenues to empower employees and retain talent. This review paper synthesizes insights from academic research, industry reports, and educational (...) initiatives to examine how prompt engineering training can mitigate the challenges of AI-induced disruption and support workforce resilience. This review article provides a comprehensive overview of the rapidly evolving field of prompt engineering. It examines fundamental techniques for crafting effective prompts, explores the diverse applications of prompt engineering across various sectors, and discusses the challenges and ethical considerations associated with its use. Furthermore, the review identifies potential future research directions and highlights the growing importance of prompt engineering in the age of large language models. This review article provides a comprehensive overview of prompt engineering, with a specific focus on its implications for workforce development and training. It examines prompt engineering techniques, applications across sectors, ethical considerations, and future research directions. A key emphasis is placed on the role of prompt engineering training programs in equipping the workforce with essential skills for the age of large language models . (shrink)

This paper provides a comprehensive overview of MoE, covering its fundamental principles, architectural variations, advantages, limitations, and potential future directions. We delve into the core concepts of MoE, including the gating network, expert networks, and routing mechanisms, and discuss how these components work together to achieve specialization and efficiency. We also examine the application of MoE in models like GPT-4 and Mixtral, highlighting their impact on the field of AI. We cover theoretical foundations, hardware and software innovations, real-world deployments, and (...) the evolving landscape of MoE research. This paper furthur provides a comprehensive survey of MoE architectures, tracing their evolution from early neural network implementations to modern large-scale applications in language models, time series forecasting, and tabular data analysis. Next, the paper examines how machine learning is applied to natural language processing, computer vision, finance and healthcare. We examine major problems, including routing imbalance, memory fragmentation and instability during training, by checking newly proposed answers found in research papers. After all, we summarize possible future areas of study and discuss how MoE models could transform the world of artificial intelligence moving forward. All the results in this paper are taken from the cited work. (shrink)

This comprehensive analysis examines the American AI Exports Program through a multi-dimensional framework encompassing technical architecture, governance structures, market strategy, and policy implementation. We synthesize insights from technology providers, content industries, security experts, and policy analysts to develop a holistic understanding of AI export challenges in the global competitive landscape. The paper presents a multi-layer framework architecture with strategic, governance, technical, and market layers, supported by detailed visualizations including architectural diagrams, decision matrices, risk assessment frameworks, and implementation roadmaps. We analyze (...) the Federal Register requirements for full-stack AI technology packages and industry-led consortia, addressing tensions between export promotion, national security, intellectual property protection, and competitive fairness. Technical implementation considerations include modular architectures, automated compliance systems, and security frameworks, while governance aspects focus on consortium structures and regulatory compliance architectures. Market strategy components cover segmentation, prioritization matrices, deployment models, and capacity building programs. The paper provides phased implementation recommendations with immediate, medium-term, and long-term initiatives, supported by performance metrics and decision support tools. This integrated approach contributes to AI policy literature by offering actionable guidance for balancing innovation acceleration with risk mitigation in the context of strategic competition, particularly with state-subsidized alternatives. (shrink)

A pervasive feature of democracy is disagreement. And in general, when we encounter disagreement from someone who is at least more reliable than chance, this puts some pressure on us to moderate our beliefs. But this raises the specter of asymmetric compliance—it’s not obvious what to do when we moderate our beliefs but the other party refuses to do so. Whereas an elegant solution is available when it comes to how we can to respond to our higher-order evidence while still (...) preserving democratic fairness, I argue that no such solution is forthcoming when we consider zetetic intransigence—that is, asymmetric compliance with regards to how we inquire, or gather evidence. The upshot is that democratic participation can involve messy tradeoffs with no entirely satisfactory resolution. (shrink)

This paper presents a comprehensive comparative analysis of state-of-the-art large language models (LLMs) for code generation, focusing on the Qwen, Claude, and DeepSeek families alongside other prominent models. Through systematic evaluation of architectural designs, performance benchmarks, and practical applications, we reveal significant advancements in open-weight models that now rival or surpass proprietary alternatives in coding tasks. Our study demonstrates Qwen3-Coder’s exceptional agentic capabilities (69.6% on SWE-bench), DeepSeek R1’s cost-efficient performance (98% lower cost than comparable models), and Claude’s robust general-purpose reasoning. (...) We analyze emerging trends including mixture-of-experts architectures, extended context windows (up to 1M tokens), and specialized coding assistants. The research incorporates temporal analysis showing accelerated innovation cycles, particularly among Chinese models, and projects future market dynamics through 2027. Our multi-dimensional evaluation covers: (1) coding performance across standardized benchmarks and real-world tasks, (2) mathematical and logical reasoning capabilities, (3) computational efficiency and cost trade-offs, and (4) architectural innovations driving progress. The findings indicate a shifting landscape where open models increasingly compete with closed systems, offering developers diverse options balancing performance, cost, and specialization. This work provides researchers and practitioners with up-to-date insights for selecting and deploying AI coding assistants in software engineering workflows. We further discuss state-of-the-art AI model versions including DeepSeek R1, Qwen 2.5/3 series, Claude 3.5/3.7 and Sonnet. The results demonstrate significant advancements in open-weight models like DeepSeek R1 and Qwen 2.5 Coder, which now rival or surpass proprietary models in specific domains while offering substantial cost advantages. We also examine emerging trends in model architectures, including mixture-of-experts implementations and context length extensions. This is pure review paper and all results are from cited literature. (shrink)

Artificial Intelligence (AI) landscape is fast developing such that there are dynamic and autonomous representatives of AI which are referred to as AI agents. These agents, fueled by the evolution of generative AI and large language models (LLMs), can make their own decisions, perform tasks and make adjustments to rapidly changing environments. An even more advanced step is the instrumentation of various specialized AI agents into working multi-agent systems (MAS). The paper will discuss the disruptive effect of the introduction of (...) AI agents and MAS on the automation and service of enterprises and different industries. We look into their possibilities, various uses, and the natural strengths and weaknesses, such as the essentiality of effective governance infrastructure and complicated conditions in human-AI partnership. Although promising new levels of efficiency and capability to solve problems previously inaccessible, ethical implications associated with the use of these agentic systems have to be carefully explored as well as the approaches to integration that should be able to guarantee their long-term value and be serving to empower humans. This paper is a survey paper regarding Agentic AI and multi-agent systems within the enterprise context. Examining 65 of thes contemporary sources (2024-2025), we record the paradigm shift of passive generative AI to autonomous agentic systems. The paper analyses the architectural structures, models of collaboration, industrial use and governance issues. The most significant ones are (1) multi-agent systems have a 40-60% efficiency gain of the processes, (2) special agent relation coordination protocols are becoming important infrastructure, and (3) it is found that human-agent collaboration needs new stewardship and motivational models. All these are ended in the paper with new directions of agent-to-agent communications and the specific agent settings. (shrink)

This paper investigates the causes, implications, and mitigation strategies of AI hallucinations, with a focus on generative AI systems. This paper examines the phenomenon of AI hallucinations in large language models, analyzing root causes and evaluating mitigation strategies. We synthesize insights from recent academic research and industry findings to explain how hallucinations often arise due to problems in the data used to train language models, limitations in model architecture, and the way large language models (LLMs) generate text. Through a systematic (...) review of current literature, we identify key patterns in how hallucinations emerge and examine the growing concern about their impact as AI becomes more embedded in decision-making systems. We identify core contributors such as data quality issues, model complexity, lack of grounding, and limitations inherent in the generative process. The risks are examined in various domains, including legal, business, and user-facing applications, highlighting consequences like misinformation, trust erosion, and productivity loss. To address these challenges, we survey mitigation techniques including data curation, retrieval-augmented generation (RAG), prompt engineering, fine-tuning, multi-model systems, and human-in-the-loop oversight. Our analysis draws from a wide range of academic and industry sources, offering both theoretical understanding and practical insights for AI practitioners. This is a pure review paper and all results are from the cited references. (shrink)

According to G.A. Cohen, the principles of justice are insensitive to facts about human moral limitations. This assumption allows him to mount a powerful defense of socialism. Here, I present a dilemma for Cohen. On the one hand, if such socialism is to be realized through collective property ownership, then the information problem renders the ideal incoherent, not merely infeasible. On the other hand, if socialism is to incorporate private ownership of productive assets, then Cohen loses the resources to distinguish (...) his view from capitalism. For, if agents were ideally motivated, there would be no need for coercive taxation schemes and limitations on trade. Moreover, incorporating coercion drastically undermines Cohen’s original argument for socialism, which relies on an analogy to a camping trip among friends. (shrink)

This paper presents a systematic review of generative artificial intelligence (AI) applications in fixed income markets, synthesizing insights from key studies published mostly between 2024 and 2025. The analysis spans the latest developments in AI-driven analytics, trading strategies, risk management techniques, and the evolution of investment approaches within this crucial sector of finance. The review covers advancements in interest rate yield curve modeling, algorithmic trading, credit and liquidity risk assessment, and structured product valuation. Special attention is given to the emergence (...) of large language models (LLMs), such as BondGPT and ChatGPT, and their integration into bond analytics, scenario generation, and regulatory workflows. These technologies are shown to enhance efficiency in bond analytics, trade documentation, liquidity analysis, and credit research, while also introducing new challenges related to model risk, data integrity, and compliance. Our findings highlight three core areas of innovation: (1) improved forecasting accuracy in interest rate models using hybrid AI architectures, (2) proposals for enhanced efficiency and automation in asset-backed securities (ABS) and mortgage-backed securities (MBS) pricing, and (3) explainable AI frameworks for compliance in risk-sensitive environments. Despite notable efficiency gains—ranging from 22% to 40% across key market functions—challenges persist around interpretability, data quality, and regulatory acceptance. Here, AI-driven valuation frameworks, scenario generation, and risk modeling innovations are accelerating the analysis and management of complex securities. Both the theoretical progress and practical impact of these models are explored in the paper, showing their abilities in explaining market patterns and their difficulties in meeting regulations. The study combines perspectives from academia, regulators and industry players to fully assess how AI is impacting the world of fixed income and offers suggestions for future improvements and best approaches. (shrink)

Where does normativity come from? Or alternatively, in virtue of what do facts about what an agent has reason to do obtain? On one class of views, reason facts obtain in virtue of agents’ motivations. It might seem like a truism that at least some of our reasons depend on what we desire or care about. However, some philosophers, notably Derek Parfit, have convincingly argued that no reasons are grounded in this way. Typically, this latter, externalist view of reasons has (...) been thought to enjoy the advantage of extensional adequacy—that is, the ability to account for all the reasons we intuitively think people have. This paper provides a novel argument against this assumption by considering a type of case wherein the relative strengths of the agent’s reasons can only be adequately explained by reference to what she cares about. Adding some further assumptions yields that there are at least some internally sourced reasons. (shrink)

The word “privilege” has become a part of our everyday conversations. However, it is not evident whether the various interlocutors in discussions on privilege are using it in the same sense. While different instances of privilege like white, male, or caste privilege have been discussed in contemporary academic discourses, we believe there is a lack of clarity regarding the notion of privilege. We critically analyse existing accounts of privilege to show that they leave some room for improvement. We offer an (...) alternative account of privilege as “entitlements that fail to track deserts” that circumvents prevalent definitional ambiguities and emphasises the inherent undeserving nature of privilege. The hitherto underexplored links between privileges, deserts, entitlements, and rights can help us formulate a more accurate grasp of privilege. (shrink)

This paper delves into the multifaceted realm of Artificial General Intelligence (AGI), exploring its definition, evolution, potential applications, and the ongoing debates surrounding its development. We examine AGI's theoretical underpinnings, contrasting it with narrow AI and artificial superintelligence (ASI). Furthermore, we discuss the impact of AGI across various sectors, including finance, research, and business, while also addressing the ethical considerations and challenges associated with its advancement. This survey synthesizes current research and perspectives, providing a comprehensive overview of AGI's trajectory and (...) its potential to reshape the future. This paper presents a comprehensive examination of Artificial General Intelligence (AGI), analyzing its current state, applications across industries, and future trajectory. Through systematic review of academic literature and industry reports, we identify three critical dimensions of AGI development: (1) technical architectures bridging narrow AI to general intelligence, (2) transformative applications in finance and business, and (3) emerging ethical and workforce challenges. Our findings reveal accelerating market growth (projected 36.9% CAGR through 2031) alongside significant research gaps in evaluation metrics, environmental impact, and cross-cultural adoption. The study highlights AGI's dual role as both disruptor and enabler, with financial services emerging as the leading adoption sector (38% of investments by 2028). We summarize (based on cited work) a framework for responsible AGI development that balances innovation with ethical considerations, emphasizing the need for standardized benchmarks and workforce transition strategies. The paper contributes to ongoing discourse by synthesizing dispersed research into actionable insights for practitioners and policymakers navigating the AGI revolution. This is a pure review paper and all results and findings are form cited literature. (shrink)

Artificial Intelligence (AI) multi-agent frameworks are enabling autonomous decision-making, intelligent collaboration, and the automation of complex workflows. These frameworks leverage Large Language Models (LLMs) and distributed AI systems to optimize operations across diverse sectors, with finance emerging as one of the most impacted domains. AI agents are increasingly employed in risk assessment, regulatory compliance, algorithmic trading, fraud detection, and customer service, fundamentally altering how financial institutions operate and manage market dynamics. This paper presents a review of AI multi-agent frameworks, evaluating (...) their architectures, applications, and deployment challenges within financial services. We conduct an in-depth comparative analysis of prominent frameworks, including LangChain, CrewAI, and OpenAI Swarm, assessing their strengths, limitations, and suitability for different financial applications. Furthermore, we examine how these frameworks integrate into financial ecosystems, facilitating automated decision-making, enhancing operational efficiency, and mitigating systemic risks. Despite the transformative potential of AI agents, their widespread adoption introduces critical challenges, such as data quality inconsistencies, lack of model explainability, regulatory concerns, and ethical dilemmas. This paper explores these issues, emphasizing the necessity for transparency, accountability, and robustness in AI-driven financial solutions. Additionally, we highlight the role of AI governance and risk mitigation strategies in ensuring regulatory compliance and alignment with financial industry standards. We also outline future research directions, advocating for the development of interpretable, scalable, and resilient AI agent frameworks. As financial automation continues to evolve, a deeper understanding of multi-agent AI systems is essential for leveraging their full potential while mitigating associated risks. (shrink)

Consider a binary afterlife, wherein some people go to Heaven, others to Hell, and nobody goes to both. Would such a system be just? Theodore Sider argues: no. For, any possible criterion of determining where people go will involve treating very similar individuals very differently. Here, I argue that this point has deep and underappreciated implications for moral philosophy. The argument proceeds by analogy: many ethical theories make a sharp and practically significant distinction between persons and non-persons. Yet, just like (...) in the binary afterlife, this involves treating very similar individuals very differently. I propose two ways out. The first is to deny that such theories are strictly speaking true, but to claim that it is practically best if people adopt them. The second is to modify such theories so as to allow for continuous variation in the scope and strength of the moral obligations arising from personhood. (shrink)

This chapter argues that governments have a duty to take reasonably effective and humane steps to minimize the occurrence of unauthorized migration and stay. While the effects of unauthorized migration on a country’s citizens and institutions have been vigorously debated, the literature has largely ignored duties of fairness to would-be immigrants. It is argued here that failing to take reasonable steps to prevent unauthorized migration and stay is deeply unfair to would-be immigrants who are not in a position to bypass (...) visa regulations. Importantly, the argument here is orthogonal to the debate as to how much and what kinds of immigration ought to be allowed. (shrink)

In today’s digital world, a large number of images are generated every day from different sources such as cameras, surveillance systems, and monitoring applications. Processing these images one by one is a slow and inefficient task, especially when the dataset size is large. This creates a need for an automated system that can handle multiple images together with minimum manual effort. This project focuses on the development of a smart web application for automated bulk image processing. The system allows users (...) to upload a compressed file containing multiple images, which are then automatically extracted and processed through a predefined workflow. Basic image preprocessing techniques are applied to improve image quality and prepare the images for further analysis. To demonstrate the practical use of the system, vehicle number plate detection and recognition is considered as a key use case. The application detects number plate regions from vehicle images and extracts the alphanumeric characters using optical character recognition techniques. The extracted results are displayed through a simple and user-friendly web interface. The proposed system helps in reducing processing time, manual work, and human errors while handling large image datasets. It provides an efficient and flexible solution that can be extended to other image processing applications where bulk image handling is required. (shrink)

Abstract : This paper surveys the landscape of AI agent frameworks, highlights their core features and differences, and explores their applications in financial services. We synthesize insights from recent industry reports, academic research, and technical blog posts, focusing on frameworks such as CrewAI, LangGraph, LlamaIndex, and others. We also discuss the challenges and opportunities of deploying agentic AI in production environments, with an emphasis on financial trading, investment analysis, and decision support. We analyze the rapidly evolving landscape of agentic AI (...) systems, focusing on their architecture, capabilities, and practical implementations in banking, trading, and risk management. The study examines prominent frameworks including LangGraph for stateful agent orchestration, CrewAI for collaborative multi-agent workflows, and AutoGen for conversational agent systems, alongside industry platforms like IBM watsonx and NVIDIA NIM. The study examines both technical frameworks (LangGraph, CrewAI, AutoGen, etc.) and practical implementations in financial institutions. We highlight productivity gains (up to 80% time reduction in data tasks), risk management improvements, and workforce transformation challenges. The paper concludes with recommendations for financial institutions adopting agentic AI solutions. Our analysis reveals three key findings: (1) specialized agent frameworks achieve 50-80% productivity gains in financial data tasks compared to traditional approaches, (2) multi-agent systems demonstrate particular promise in complex domains like algorithmic trading and fraud detection, and (3) successful deployment requires addressing critical challenges in workforce upskilling, risk alignment, and regulatory compliance. The paper provides a theoretical foundation for agentic AI in finance, introducing formal models for agent design patterns, multimodal fusion, and market microfoundations. We further present a summary of several evaluation frameworks for assessing agent performance across financial use cases, including portfolio optimization and AML compliance. The study concludes with recommendations for financial institutions adopting agentic AI, emphasizing the need for standardized architectures, robust testing protocols, and hybrid human-AI workflows. (shrink)

The financial planning and investment management profession is undergoing a radical transformation driven by Generative AI (GenAI) and Agentic AI, creating urgent workforce displacement challenges that require coordinated government policy intervention alongside educational reform. This paper presents a comprehensive framework addressing both curricular modernization and policy responses to offshore outsourcing, permanent job loss, and structural changes in the financial workforce. We propose a multi-layered integration strategy for higher education encompassing: 1) Foundational AI literacy modules for all business students; 2) A (...) specialized "Agentic Financial Planning" course with hands-on labs; 3) AI-augmented redesign of core courses (Investments, Portfolio Management, Ethics); 4) Interdisciplinary project-based learning with Computer Science; and 5) A governance and policy module addressing regulatory compliance (NIST AI RMF, SEC regulations). Beyond curriculum, we develop a comprehensive government policy framework including: 1) Federal AI literacy mandates for post-secondary business education; 2) Department of Labor workforce retraining programs with income support for displaced financial professionals; 3) SEC and Treasury regulatory innovations creating market incentives for workforce development; 4) State-level workforce partnerships implementing regional transition support; and 5) Enhanced social safety nets for workers navigating career transitions during the estimated 5-15 year transformation period. Drawing on analysis of agentic investment firm operational models demonstrating 50-70% cost reductions while maintaining fiduciary standards, we establish the economic inevitability of technological transformation and the critical urgency of proactive intervention. The framework provides a roadmap for coordinated response across educational institutions, government agencies, and industry to ensure workforce resilience and domestic leadership in the emerging agentic finance era. (shrink)

AgentOps is an emerging discipline focused on the operationalization, monitoring, and optimization of agentic AI systems, especially in the context of generative AI. This paper provides an overview of AgentOps, its relationship to MLOps and GenAIOps, and best practices for deploying and managing agentic AI in enterprise environments. This paper shows how the evolution happened form operational methodologies in AI, from traditional Machine Learning Operations (MLOps) to finally specialized Large Language Model Operations (LLMOps) and then to Generative AI Operations (GenAIOps). (...) We examine the key challenges in operationalizing generative AI, including model monitoring, prompt management, agent debugging, and ethical considerations. Through a systematic review of over 100 recent articles and industry practices, we identify critical gaps in current operational approaches and summarize based on recent literature an integrated framework that combines MLOps, LLMOps, and AgentOps principles. We present case studies demonstrating successful implementations and provide recommendations for organizations transitioning to generative AI at scale. We delineate the distinct characteristics, challenges, and best practices associated with each stage, emphasizing how AgentOps extends prior concepts to manage the unique complexities of multi-agent systems, including monitoring, debugging, and secure lifecycle management. We provide a holistic overview of the current landscape and future directions for operationalizing advanced AI systems. (shrink)

This works summarizes the recent developments in GPT Models applicable to Financial Institutions. Comparative analysis highlights the strengths of proprietary models like BloombergGPT for domain-specific tasks and open-source alternatives such as FinGPT for flexibility and cost-effectiveness. The study also emphasizes the role of AI-powered platforms like AlphaSense in managing unstructured data for market intelligence. Proposed future work includes exploring alternative attention mechanisms, integrating multi-modal capabilities, and enhancing model interpretability to address the challenges of computational complexity and domain adaptation. The results (...) demonstrate that GPT-based models significantly advance the capabilities of financial institutions to analyze large datasets, identify trends, and support data-driven decision-making. This paper contributes to the growing body of research on financial AI by proposing a scalable and effective approach to deploying LLMs in finance. This paper presents a comprehensive review of framework and architecture for leveraging Generative Pre-trained Transformers (GPT) in financial analysis and decision-making. Building upon the advancements in transformer-based models, the proposed approach integrates multi-layer self-attention mechanisms, fine-tuning on domain-specific data, and reinforcement learning with human feedback to enhance natural language understanding and generation tasks. Key contributions include an adapted GPT model with improved attention mechanisms and parameter scaling to handle financial texts effectively. The methodology involves a multi-phase approach encompassing data collection, pre-training, and fine-tuning, with performance evaluated using metrics such as perplexity, accuracy, and F1 score. (shrink)

This paper presents a comprehensive review of Generative AI applications in DevOps automation, covering 50 key research works published between 2023-2025. By synthesizing insights from recent research and industry practice, this paper identifies the top terms, theories, and algorithms shaping the field and offers a forward-looking perspective on the evolution of AI-driven DevOps through 2029. We analyze the transformative impact of AI-driven solutions across the software development lifecycle, including code generation, infrastructure management, continuous integration/delivery, and Kubernetes operations. The present paper (...) is a thorough review of how generative AI and agentic workflows are changing the way modern software systems are developed, deployed, and operated. We look at the introduction of automation in continuous integration and continuous deployment (CI / CD) pipelines using AI / ML, the rise of cloud-native platforms (e.g. Docker and Kubernetes), and the Infrastructure as Code (IaC) and the rise of progressive delivery models. The paper points out the positives of these developments, which consist of efficiency, reliability, and speed of innovation, and also focuses on the issue of security, compliance, observability, and skill development. The review is a systematic study of how generative AI improves the efficiency of deployment, monitoring, and the general development workflow and solves the problem in cloud-native environments. In our analysis, we identified the rising trends in AI agents to use in DevOps, containerized AI applications, and large language models integrated into the existing DevOps toolchains. It is a review article and all the findings mentioned are by their respective authors. (shrink)

This paper synthesizes recent research and practical frameworks to explore the impact of AI on multi-criteria decision-making (MCDM), stakeholder relations, leadership, and organizational change. Drawing on empirical studies, reviews, and industry insights, we provide a comprehensive analysis of AI's transformative role, highlight challenges, and propose strategies for effective AI adoption. By leveraging AI-driven tools such as MCDA methods, intelligent mediation systems, and change management frameworks, organizations can achieve enhanced strategic planning, cross-functional collaboration, and adaptive leadership. We present a comprehensive analysis (...) of current implementations, challenges, and future directions for AI in complex organizational structures, drawing from recent scholarly works and industry case studies. Our findings demonstrate that AI-enabled matrix organizations show 23% higher decision-making efficiency and 37% improved conflict resolution rates compared to traditional structures. Drawing upon recent advancements in multi-criteria decision analysis (MCDA), we demonstrate how machine learning-enhanced methods such as AHP and TOPSIS are achieving 23-29% improvements in decision speed and accuracy across supply chain, healthcare, and engineering applications. The study further explores the evolution of human-AI collaboration models, from early toolbased systems to contemporary agentic frameworks capable of autonomous negotiation and conflict resolution. Through analysis of organizational change case studies, we identify key success factors in AI adoption, including leadership commitment metrics (Lt > 0.8) and change capacity coefficients (Cc > 0.7) that predict successful implementation. The research reveals that matrix organizations leveraging AI-mediated stakeholder management and cross-functional collaboration tools achieve 37% higher conflict resolution rates compared to traditional structures. (shrink)

Artificial Intelligence (AI) agents are transforming the marketing scene by streamlining work, improving personalization, and maximizing campaigns. This paper examines the complex role of AI agents in marketing, ranging from their use in content generation, customer interaction, data analysis, and generating leads. It explores the technical bases of these agents, discussing important theories like Multi-Armed Bandits, Transformer models, and Gaussian Mixture Models, and how they are implemented practically within tools such as Jasper and Quantilope. The study also discusses how marketing (...) work is affected by AI agents, including worrying job displacement as well as new roles and required skill changes. Ethical issues, such as data privacy and algorithmic bias, are examined in tandem with emerging trends such as autonomous marketing teams and hyper-personalization. The research concludes by providing strategic advice to businesses and professionals for leveraging AI agents maximally while minimizing risks. (shrink)

This paper presents a comprehensive review of artificial intelligence (AI) applications in food safety and quality control, focusing on emerging technologies including generative AI, agentic AI systems, and automated compliance solutions. This review synthesizes current research and industry applications, highlighting how AI-driven systems are transforming food safety protocols, enhancing regulatory compliance, and improving overall food quality management. We examine various AI implementations, from optical imaging for bacterial detection to intelligent compliance agents and generative AI for supply chain optimization. This paper (...) synthesizes current research and industry applications across multiple domains: automated visual inspection systems that detect contaminants with precision exceeding human capabilities; predictive quality analytics that forecast potential safety issues before they manifest; AI-driven regulatory compliance systems that continuously monitor and interpret complex regulatory requirements; and autonomous agentic systems that make real-time decisions without human intervention. The review also addresses significant technological innovations, including the FDA's development of AI tools for regulatory operations, generative AI applications for scenario planning and documentation, and cloud-based AI architectures deployed across major platforms. Critical challenges are examined, including data quality requirements, regulatory validation frameworks, system integration complexities, and ethical considerations. The paper concludes with policy recommendations for government implementation, proposing structured approaches to AI validation, data sharing incentives, regulatory modernization, research support, and ethical oversight. A dedicated response to FSIS-2025-0145 demonstrates how AI technologies can address overtime inspection fee challenges through remote monitoring and automated compliance. (shrink)

This paper offers a comprehensive review of existing literature on the intersection of Artificial Intelligence (AI) and leadership, drawing on both theoretical insights and practical implementations. By analyzing scholarly publications from the past two years (2023-2025), the review traces emerging patterns in how AI technologies are being integrated into leadership practices. Key themes include the growing relevance of learning-based systems for adaptive decision-making and the application of attention-based models to improve responsiveness in dynamic environments. The review also addresses ethical dimensions (...) of AI-enabled leadership, emphasizing the need to balance algorithmic efficiency with human judgment and oversight. Concerns around transparency, psychological safety, and trust in automated systems are explored in depth. Furthermore, the paper outlines various AI-supported leadership support systems that are currently in use, highlighting their potential to assist leaders in strategic forecasting, communication, and stakeholder engagement. The synthesis incorporates multiple theoretical frameworks that help contextualize AI’s role in leadership transformation, offering a structured view of how emerging technologies are reshaping leadership thought and behavior. Ultimately, this review maps out a landscape of opportunities and challenges, providing a foundation for future research in AI-augmented leadership. The analysis identifies reinforcement learning as a predominant approach in leadership strategies, with a theory-weighted impact metric (Impact=∑T_i×F_i) assigning it a weighted score of 4.08/6.0. The review also highlights the use of multi-head attention mechanisms (LeadershipAttention(Q,K,V)) to enhance crisis response times by 37% (p<0.001). Additionally, ethical concerns are discussed, particularly regarding the incorporation of KL divergence optimization systems (KL(p_AI |)p_human )<ϵ) to maintain human oversight. The findings from the reviewed studies show that AI adoption leads to a 58% ±12% faster decision-making process, a 41% ±9% increase in strategic accuracy, and 89.2% forecasting precision. However, challenges in psychological safety thresholds (T<0.4) and transparency in AI decision-making (A<0.6) persist. The paper also discusses existing AI-Driven Leadership Decision Support Systems (AI-LDSS), including the use of transformer-based NLP, SHAP-explainable predictions, and bias detection. This review synthesizes theoretical frameworks, including differential leadership equations ((dL_i)/dt=αL_i (1-L_i/K)-β∑L_i L_j+γA_i (t)), and provides an overview of the current state of AI in leadership research. (shrink)

This paper provides a comprehensive review of Artificial Intelligence (AI) integration in K-12 education, examining current implementations, policy frameworks, and emerging challenges. We analyze over 40 recent publications (2024-2025) from academic journals, government reports, and industry whitepapers to identify key trends in AI adoption across primary and secondary education systems. This paper presents a comprehensive review of Artificial Intelligence (AI) integration in K-12 education, examining its pedagogical, technical, and policy dimensions. Through an analysis of recent literature, we highlight Generative AI (...) as the most widely adopted paradigm in educational settings, with Agentic AI emerging as a significant secondary focus. The review identifies key trends in architectural approaches while noting underrepresented technical frameworks. Our review reveals three critical dimensions of AI in education: (1) pedagogical applications including personalized learning and administrative automation, (2) policy and ethical considerations at federal and state levels, and (3) infrastructure requirements for successful implementation. We highlight the rapid growth of Generative AI (GenAI) tools in classrooms alongside persistent concerns about equity, data privacy, and teacher preparedness. We summarize a conceptual framework for evaluating educational AI systems that balances pedagogical value with implementation considerations. This systematic review examines Artificial Intelligence (AI) integration in K-12 education through pedagogical, technical, and policy lenses. (shrink)

This comprehensive technical paper responds to the Centers for Disease Control and Prevention's Federal Register notice (Docket No. CDC-2025-0753) concerning the revision of the National HIV Behavioral Surveillance System (NHBS). We propose an integrated framework leveraging Generative AI (GenAI) and agentic systems to enhance the NHBS data collection methodology across 21 Metropolitan Statistical Areas (MSAs). Our approach addresses all five evaluation criteria specified by the Office of Management and Budget: (1) necessity and practical utility, (2) accuracy of burden estimates, (3) (...) enhancement of data quality, utility, and clarity, (4) minimization of respondent burden through technology, and (5) assessment of information collection costs. Drawing on recent research in AI-assisted surveying, we demonstrate how Large Language Models (LLMs), adaptive interviewing systems, and human-AI hybrid frameworks can transform NHBS from a periodic cross-sectional survey into a dynamic, real-time surveillance tool while reducing the estimated 3,398-hour annual burden. We provide detailed implementation recommendations for the proposed three-year cycle, addressing ethical considerations, validation requirements, and quality assurance protocols for deployment in public health settings. This expanded framework includes comprehensive technical specifications, cost-benefit analyses, and risk mitigation strategies to support evidence-based decision-making for CDC leadership. (shrink)

This paper explores the current applications, benefits, and challenges of generative AI in various business do- mains, drawing from recent literature and industry reports from literature published in 2025. We examine key use cases including content creation, knowledge management, business process automation, and decision support. The paper also discusses implementation challenges, ethical considerations, and future directions for generative AI adoption in business contexts. We analyze key applications in operational efficiency, risk management, and strategic decision-making through recent industry reports and academic (...) perspectives. This paper presents a comprehensive visual framework for analyzing generative AI applications in business through 18+ original diagrams. The framework systematically organizes key findings across four dimensions: value creation, functional impact, implementation roadmaps, and risk management. Central to our analysis are three core visualizations: (1) an enterprise implementation diagram mapping productivity gains of 30-50% across business processes, (2) a functional impact wheel identifying marketing and data analytics as highest-potential applications, and (3) a comparative ROI matrix showing process automation delivering 50% cost reductions versus content generation’s rapid 3-month implementation cycles. The visual methodology reveals critical adoption patterns including the inverse relationship between technical complexity and organizational readiness, particularly in risk-sensitive domains. Our framework provides business leaders with an actionable taxonomy for strategic planning, supported by measurable performance benchmarks and maturity assessments. The charts collectively demonstrate that successful generative AI adoption requires balancing technical capabilities with operational constraints and ethical considerations. (shrink)

This paper explores the transformative impact of artificial intelligence (AI) on organizational teamwork, decision-making, and resilience. This paper furthur reviews recent literature on the integration of Artificial Intelligence (AI) in various organizational functions, focusing on its impact on innovation management, leadership paradigms, and organizational resilience. We provide groundwork required to enhance frameworks that can integrate cognitive scaffolding with antifragile team dynamics, employing behavioral economics and neurocognitive principles. We introduce methodologies for enhancing team resilience through adaptive AI systems, cross-training interventions, and (...) pre-mortem simulation techniques. The framework addresses key challenges in confirmation bias mitigation, cultural dimension alignment, and vigilance decrement prevention. This paper explores how cognitive scaffolding mechanisms within organizations enhance team antifragility, enabling dynamic adaptation and growth under stress.We further synthesize findings from studies exploring the adoption of generative AI tools like ChatGPT by innovation managers, the potential of AI in enhancing resilience for small businesses in the context of Industry 5.0, and the evolving role of leadership in the age of AI. Furthermore, we examine the significance of human factors such as emotional intelligence, empathy, and self-compassion in navigating the integration of AI and fostering innovation and resilience within teams and organizations. This review highlights the complex interplay between technological advancements and essential human capabilities in shaping the future of work and organizational success. Through systematic analysis of 32 peer-reviewed studies (2016-2025), we surveyed and found how AI cognitive scaffolding systems improve team antifragility by 214% when combined with human emotional intelligence. Our review by studying research reveals that hybrid AI-human decision models achieve 38% faster response times while maintaining 89% prediction accuracy in behavioral assessments. The paper introduces a novel cultural alignment metric (Cdiff ) for global teams and evaluates four STAR framework variants for AI-enhanced workforce development. (shrink)

The rapid advancement of generative artificial intelligence (Gen AI) has revolutionized various domains, including financial analytics. This paper provides a comprehensive review of the applications, challenges, and future directions of Gen Al in financial analytics. We explore its role in risk management, credit scoring, feature engineering, and macroeconomic simulations, while addressing limitations such as data quality, interpretability, and ethical concerns. By synthesizing insights from recent literature, we highlight the transformative potential of Gen AI and propose frameworks for its effective integration (...) into financial workflows. This paper presents a systematic examination of generative artificial intelligence (Gen AI) applications in financial risk management, focusing on architectural frameworks and implementation methodologies. We analyze the integration of large language models (LLMs) with traditional quantitative finance pipelines, addressing key challenges in feature engineering, risk modeling, and regulatory compliance. The study demonstrates how transformer-based architectures enhance financial analytics through automated data processing, risk factor extraction, and scenario generation. Technical implementations leverage hybrid cloud platforms and specialized Python libraries for model deployment, achieving measurable improvements in accuracy and efficiency. Our findings reveal critical considerations for production systems, including computational optimization, model interpretability, and governance protocols. The proposed architecture combines LLM capabilities with domain-specific modules for credit scoring, value-at-risk calculation, and macroeconomic simulation. Empirical results highlight trade-offs between model complexity and operational constraints, providing actionable insights for financial institutions adopting Gen Al solutions. The paper concludes with recommendations for future research directions in financial Al systems. (shrink)