Regulation Of Artificial Intelligence Research Articles

Smart Robots and Civil Liability in Jordan: A Quest for Legal Synthesis in the Age of Automation

This paper embarks on a pivotal scholarly journey to scrutinize the civil liability implications of Artificial Intelligence (AI) within the Jordanian legal framework. As AI transforms from a speculative concept into an omnipresent reality, it presents unique jurisprudential challenges, particularly civil liability. This study aims to bridge the jurisprudential vacuum in Jordan, where an absence of judicial precedents highlights the urgency for legal exploration in this emerging field. Adopting a doctrinal legal research methodology, the study rigorously examines Jordanian liability theories and their applicability to AI and undertakes a comparative analysis with more advanced legal systems in AI regulation. The paper navigates the intricacies of AI in the context of traditional Jordanian legal theories, such as objective and personal liability theories. It extends to explore the challenges of attributing liability in the age of AI. Key findings reveal significant inadequacies in Jordan’s current legal provisions to address AI-induced liabilities, the disconnection between civil liability and AI accountability, jurisdictional ambiguities, and challenges in applying traditional legal concepts to the unpredictable nature of AI. The study proposes granting AI legal personality, legislative intervention, enforcing global accountability standards, advocating for international cooperation, and exploring technological limitations with ethical programming. This research contributes profoundly to academic discourse and policy formulation, particularly in the Jordanian context, highlighting the need for a comprehensive, adaptable, and robust legal framework that addresses the unique challenges posed by AI and robotics in the legal domain.

Perception of the risks inherent in new AI technologies

Artificial intelligence (AI) has undergone rapid development and is becoming one of the major social issues. The advent of generative AI is not only associated with potential benefits but also poses a number of risks, such as increasing malicious misuse. This brings the issue of regulating new AI technologies into focus. In the search for solutions, the article revisits the problem of applying the precautionary principle. We critically evaluate regulatory approaches, particularly with regard to maintaining an innovation-friendly environment. A prudent approach to new AI technologies not only requires regulatory measures but also places new demands on the education system. We also discuss the regulation of AI from the perspective of Czech legislation in a broader international context. Current challenges in the field of AI competence are highlighted. To make the issue more tangible, we provide specific examples from the Czech Republic.

Assessment of the impacts of artificial intelligence (AI) on intercultural communication among postgraduate students in a multicultural university environment

Artificial intelligence (AI) broadly influences different aspects of human life, especially human communication. One of the main concerns of the broad use of AI in daily interactions among different people could be whether it helps them interact easily or complicates their interactions. To answer the mentioned question, this study assessed the impacts of AI on intercultural communication among postgraduate students in a multicultural university environment. A newly developed survey instrument was used to conduct this study. The participants of this study were 115 postgraduate students from nine different countries. The descriptive statistics, reliability analysis, and Bivariate correlation tests of the 29th version of IBM-SPSS software were used to analyze the quantitative data, and inductive coding and conceptual content analysis were used to code and analyze the qualitative data. Based on descriptive results, the vast majority (93%) of the participants already used and experienced AI in their daily lives, and the majority of them believed that AI and AI technologies connect different cultures, reduce language and cultural barriers, and help people of different cultures to interact and be connected. Based on the results from the correlation test, there were strong positive correlations between AI attitudes and AI benefits, and also between AI regulation and AI benefits.

Artificial intelligence co-regulation? The role of standards in the EU AI Act

Abstract This article examines artificial intelligence (AI) co-regulation in the EU AI Act and the critical role of standards under this regulatory strategy. It engages with the foundation of democratic legitimacy in EU standardization, emphasizing the need for reform to keep pace with the rapid evolution of AI capabilities, as recently suggested by the European Parliament. The article highlights the challenges posed by interdisciplinarity and the lack of civil society expertise in standard-setting. It critiques the inadequate representation of societal stakeholders in the development of AI standards, posing pressing questions about the potential risks this entails to the protection of fundamental rights, given the lack of democratic oversight and the global composition of standard-developing organizations. The article scrutinizes how under the AI Act technical standards will define AI risks and mitigation measures and questions whether technical experts are adequately equipped to standardize thresholds of acceptable residual risks in different high-risk contexts. More specifically, the article examines the complexities of regulating AI, drawing attention to the multi-dimensional nature of identifying risks in AI systems and the value-laden nature of the task. It questions the potential creation of a typology of AI risks and highlights the need for a nuanced, inclusive, and context-specific approach to risk identification and mitigation. Consequently, in the article we underscore the imperative for continuous stakeholder involvement in developing, monitoring, and refining the technical rules and standards for high-risk AI applications. We also emphasize the need for rigorous training, certification, and surveillance measures to ensure the enforcement of fundamental rights in the face of AI developments. Finally, we recommend greater transparency and inclusivity in risk identification methodologies, urging for approaches that involve stakeholders and require a diverse skill set for risk assessment. At the same time, we also draw attention to the diversity within the European Union and the consequent need for localized risk assessments that consider national contexts, languages, institutions, and culture. In conclusion, the article argues that co-regulation under the AI Act necessitates a thorough re-examination and reform of standard-setting processes, to ensure a democratically legitimate, interdisciplinary, stakeholder-inclusive, and responsive approach to AI regulation, which can safeguard fundamental rights and anticipate, identify, and mitigate a broad spectrum of AI risks.

Ensuring AI Accountability: The Need for Expedited Oversight Frameworks Based On EU

The inevitability of artificial intelligence (AI) impacting every facet of our life is already evident in many ways. Nations are contending with the prospects and difficulties that AI offers. India has emerged as a frontrunner among South Asian nations in spearheading the promotion and regulation of AI. Nevertheless, it falls far behind nations like China or the United States. This article examines the AI ecosystem in India, including the risks and problems it encounters, as well as the ethical concerns it must address. Ultimately, it analyses the need for a comprehensive framework based on the EU regulation for a better control and regulation.

Compromise in multilateral negotiations and the global regulation of artificial intelligence

ABSTRACT As artificial intelligence (AI) technologies spread worldwide, international discussions have increasingly focused on their consequences for democracy, human rights, fundamental freedoms, security, and economic and social development. In this context, UNESCO’s Recommendation on the Ethics of Artificial Intelligence, adopted in November 2021, has emerged as the first global normative framework for AI development and deployment. The intense negotiations of every detail of the document brought forth numerous controversies among UNESCO member states. Drawing on a unique set of primary sources, including written positions and recorded deliberations, this article explains the achievement of global compromise on AI regulation despite the multiplicity of UNESCO member-state positions representing a variety of liberal and sovereignist preferences. Building upon Boltanski’s pragmatic sociology, it conceptualizes the practice of multilateral negotiations and attributes the multilateral fragile agreements to two embedded therein compromises: Structural normative hybridity and situated normative ambiguity allowed to accomplish a compromise by linking macro-normative structures with situated debates of multilateral negotiations.

Responsible AI-Based Business Process Management and Improvement

Organizations today seek to improve and adapt their business processes because of an increasingly competitive economy. The use and application of Artificial Intelligence (AI) for business process improvement and management is often discussed and put in place, regardless of its potentially negative impact if AI is implemented in the wrong way, especially around the processing and storing of personal data. We discuss the use of AI for the management and improvement of business processes, especially in the financial domain, and how to ensure responsible AI use in enterprises for this aim. We propose an information system design for responsible and trustworthy business processes, and we envision that businesses will need strong and well-defined control points in their information systems for managing processes and creating associated audits to enforce their principles. We define questions and challenges that companies will need to reflect upon and follow to achieve an application of responsible AI in an enterprise context. We also outline considerations for AI and data protection regulation for companies, while also considering the technical challenges that would need to be solved.

China's Interim Measures on generative AI: Origin, content and significance

On 15 August 2023, China's new rules on generative artificial intelligence (AI) entered into force. This article explores the underlying reasons and context for this rapid regulatory development. It argues that China's swift adoption of the Interim Measures on generative AI has been enabled by its traditional approach to digital policy, together with its renewed system of governance and the extensive work that Chinese regulators had conducted on AI ethics and relevant principles. The article also analyses some of the substantial rules laid down by the Interim Measures, offering scholars and policymakers working on generative AI regulation in other jurisdictions the possibility to engage with the solutions chosen by the Chinese regulators. To this end, the article brielfy presents key provisions regarding training data and IP rights; labelling of synthetic content; algorithm registration; accountability for content; and the applicability of existing laws to generative AI. It compares these aspects of the Interim Measures with examples from the European Union and the United States.

Possible harms of artificial intelligence and the EU AI act: fundamental rights and risk

Various actors employ the notion of risk when they discuss the future role of Artificial Intelligence (AI) in society – sometimes as a general pointer to possible unwanted consequences of the underlying technologies, sometimes oriented towards a political regulation of AI risks. Mostly discussed within a legal or ethical framework, we still lack a perspective on AI risks based on sociological risk research. Building on systems-theoretical thinking about risk and society, this article analyses the potential and limits of a risk-based regulation of AI, in particular with regard to the notion of harm to fundamental rights. Drawing on the AI Act, its earlier drafts and related documents, the paper analyses how this regulatory framework delineates harms of AI and which implications the chosen delineation has for the regulation. The results show that fundamental rights are invoked as legal rules, as values and as a foundation for trustworthiness of AI in parallel to being identified as at risk from AI. The attempt to frame all possible harms in terms of fundamental rights creates communicative paradoxes. It opens the door to a political classification of high-risk AI systems as well as a future standard-setting that is removed from systematic concerns about fundamental rights and values. The additional notion of systemic risk, addressing possible risks from general-purpose AI models, further reveals the problems with delineating harms of AI. In sum, the AI Act is unlikely to achieve what it aims to do, namely the creation of conditions for trustworthy AI.

Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders.

To provide a brief overview of artificial intelligence (AI) application within the field of eating disorders (EDs) and propose focused solutions for research. An overview and summary of AI application pertinent to EDs with focus on AI's ability to address issues relating to data sharing and pooling (and associated privacy concerns), data augmentation, as well as bias within datasets is provided. In addition to clinical applications, AI can utilize useful tools to help combat commonly encountered challenges in ED research, including issues relating to low prevalence of specific subpopulations of patients, small overall sample sizes, and bias within datasets. There is tremendous potential to embed and utilize various facets of artificial intelligence (AI) to help improve our understanding of EDs and further evaluate and investigate questions that ultimately seek to improve outcomes. Beyond the technology, issues relating to regulation of AI, establishing ethical guidelines for its application, and the trust of providers and patients are all needed for ultimate adoption and acceptance into ED practice. Artificial intelligence (AI) offers a promise of significant potential within the realm of eating disorders (EDs) and encompasses a broad set of techniques that offer utility in various facets of ED research and by extension delivery of clinical care. Beyond the technology, issues relating to regulation, establishing ethical guidelines for application, and the trust of providers and patients are needed for the ultimate adoption and acceptance of AI into ED practice.

Responsibly Buying Artificial Intelligence: A ‘Regulatory Hallucination’

Abstract As part of its ‘pro-innovation’ approach to artificial intelligence (AI), the UK has left public sector AI procurement and deployment to ‘regulation by contract’ based on thin guidance. Borrowing from the description of AI ‘hallucinations’ as plausible but incorrect answers given with high confidence by AI systems, I argue that this is a ‘regulatory hallucination’: an incorrect answer to the challenge of regulating the procurement and use of AI by the public sector. The pretence that public buyers can ‘confidently and responsibly procure AI technologies’ can generate individual harms and broader negative social effects as the public sector ramps up AI adoption and accumulates a potentially significant stock of AI deployments across all areas of public sector activity. I sketch an alternative strategy to boost the effectiveness of the goals of AI regulation and the protection of individual rights and collective interests through the creation of an independent authority.

소위 ‘약한 인공지능(AI)’ 영역에서의 민사책임에 관한 연구

Now, the core of the discussion is how artificial intelligence (AI), as a rapidly developing advanced technology, can solve many of the most difficult problems facing humanity beyond solving severe social problems. Therefore, the true value of artificial intelligence (AI) as a cutting-edge technology is to solve huge problems facing humanity, such as social problems such as gun violence, food shortages, incurable diseases, and global warming caused by carbon emissions, and to contribute to human development. The concern, though, is that no one knows exactly how current artificial intelligence (AI) works. In other words, it is similar to developing a bomb that does not know when it will explode without a safety device.
 Already, artificial intelligence (AI) is causing damage by providing false information. So, if we don't know how to control better artificial intelligence (AI) when it appears, dangerous things can happen. Therefore, there will be a need for international norms on artificial intelligence (AI), which will be made possible by international treaties. The European Union has already enacted the AI Act as the first regulatory law on AI. This contains stronger regulations on the AI industry than in the United States or Asia. In particular, the Act stipulates the obligation to display AI-generated content. Thus, the EU's AI regulation law can be evaluated as legislation designed to ensure the safety of AI-generated products, such as cars and toys. In addition, in September 2022, the European Union (EU) issued a directive on AI accountability. It focuses on the legal principles of compensation for damages caused by artificial intelligence (AI). 
 Legislation on punitive damages for deepfake harms is required. As a civil liability, the victim's damages should be recognized not only for mental damages, but also for aggravated damages for monetary damages, if any. As such a legislative measure, so-called punitive damages should be introduced for malicious and deliberate use of artificial intelligence (AI) as a perpetrator. In the so-called risk liability of artificial intelligence (AI), the operator of artificial intelligence (AI), which has enormous profits through the operation of the risk source, will have to bear the risk liability as a no-fault liability. Korea's product liability law has embraced this principle of risk liability. In order for product liability law to apply to artificial intelligence (AI), there must be a defect in the artificial intelligence (AI) installed in the product.

Advancements and Applications of Generative Artificial Intelligence and Large Language Models on Business Management: A Comprehensive Review

This comprehensive review delves into the landscape and recent advancements of Generative Artificial Intelligence (AI) and Large Language Models (LLMs), shedding light on their transformative potential and applications across various sectors. Generative AI, exemplified by models like ChatGPT, DALL-E, and Midjourney, has rapidly evolved and is driven by breakthroughs in deep learning architectures and the availability of vast datasets. Concurrently, LLMs have revolutionized natural language processing tasks, utilizing vast text corpora to generate human-like text. The study explores recent developments, including the introduction of advanced models like GPT-4 and PaLM2 and the emergence of specialized LLMs like small LLMs (sLLMs), aimed at overcoming hardware limitations and cost constraints. Additionally, the expanding applications of generative AI, from healthcare to finance, underscore its transformative potential in addressing real-world challenges. Through a comprehensive analysis, this research contributes to the ongoing discourse on AI ethics, governance, and regulation, emphasizing the importance of responsible innovation for the benefit of humanity.

Artificial Intelligence Technologies and Practical Normativity/Normality:Investigating Practices beyond the Public Space.

This essay examines how artificial intelligence (AI) technologies may shape international norms. Following a brief discussion of the ways in which AI technologies pose new governance questions, we reflect on the extent to which norm research in the discipline of International Relations (IR) is equipped to understand how AI technologies shape normative substance. Norm research has typically focused on the impact and failure of norms, offering increasingly diversified models of norm contestation, for instance. But present research has two shortcomings: a near-exclusive focus on modes and contexts of norm emergence and constitution that happen in the public space; and a focus on the workings of a pre-set normativity (ideas of oughtness and justice) that stands in an unclear relationship with normality (ideas of the standard, the average) emerging from practices. Responding to this, we put forward a research programme on AI and practical normativity/normality based on two pillars: first, we argue that operational practices of designing and using AI technologies typically performed outside of the public eye make norms; and second, we emphasise the interplay of normality and normativity as analytically influential in this process. With this, we also reflect on how increasingly relying on AI technologies across diverse policy domains has an under-examined effect on the exercise of human agency. This is important because the normality shaped by AI technologies can lead to forms of non-human generated normativity that risks replacing conventional models about how norms matter in AI-affected policy domains. We close with sketching three future research streams. We conclude that AI technologies are a major, yet still under-researched, challenge for understanding and studying norms. We should therefore reflect on new theoretical perspectives leading to insights that are also relevant for the struggle about top-down forms of AI regulation.

The Global Institutional Governance of AI: A Four-Dimensional Perspective

Abstract The present debate about the governance of artificial intelligence (AI) is dominated by a narrative of a “global race toward the regulation of AI.” Such a narrative bears serious dangers and should be rephrased as the “race toward the global regulation of AI” to adequately address the cross-cutting, cross-boundary, and cross-cultural nature of these technologies. If the debate about the future regulation of AI is to efficiently address the serious dangers and potentially existential risks related to AI, then it should be tied to other global governance issues, such as those summarized by the United Nations Sustainable Development Goals (SDGs). For this endeavor to be successful, the substantive questions of regulation must be combined with efforts to reform the present international system with a view to establishing a more efficient and coherent global institutional framework. It is important to be mindful of past obstacles in the reform of existing international organizations and to avoid the need for another global cataclysm to trigger institutional reform; thus, the article follows the idea that cognitive change leads to the transformation of international organizations. As both a technology aimed to replicate the human mind and an example of an important linguistic trend of a rise in essentially oxymoronic concepts, AI is deemed to provide the right point of departure to ponder future modes of human cognition – modes that reflect Einstein’s description of a world as a “four-dimensional space – time continuum,” – which may help to imagine the contours of a future global institutional framework.

AI regulation in the European Union: examining non-state actor preferences

AbstractAs the development and use of artificial intelligence (AI) continues to grow, policymakers are increasingly grappling with the question of how to regulate this technology. The most far-reaching international initiative is the European Union (EU) AI Act, which aims to establish the first comprehensive, binding framework for regulating AI. In this article, we offer the first systematic analysis of non-state actor preferences toward international regulation of AI, focusing on the case of the EU AI Act. Theoretically, we develop an argument about the regulatory preferences of business actors and other non-state actors under varying conditions of AI sector competitiveness. Empirically, we test these expectations using data from public consultations on European AI regulation. Our findings are threefold. First, all types of non-state actors express concerns about AI and support regulation in some form. Second, there are nonetheless significant differences across actor types, with business actors being less concerned about the downsides of AI and more in favor of lax regulation than other non-state actors. Third, these differences are more pronounced in countries with stronger commercial AI sectors. Our findings shed new light on non-state actor preferences toward AI regulation and point to challenges for policymakers balancing competing interests in society.

Regulation of algorithms in Artificial Intelligence systems: a possible proposal for Brazil?

Regulation of algorithms in Artificial Intelligence systems: a possible proposal for Brazil?

Managing risk and resilience in autonomous and intelligent systems: Exploring safety in the development, deployment, and use of artificial intelligence in healthcare.

Autonomous and intelligent systems (AIS) are being developed and deployed across a wide range of sectors and encompass a variety of technologies designed to engage in different forms of independent reasoning and self-directed behavior. These technologies may bring considerable benefits to society but also pose a range of risk management challenges, particularly when deployed in safety-critical sectors where complex interactions between human, social, and technical processes underpin safety and resilience. Healthcare is one safety-critical sector at the forefront of efforts to develop and deploy intelligent technologies, such as through artificial intelligence (AI) systems intended to automate key aspects of healthcare tasks such as reading medical images to identify signs of pathology. This article develops a qualitative analysis of the sociotechnical sources of risk and resilience associated with the development, deployment, and use of AI in healthcare, drawing on 40 in-depth interviews with participants involved in the development, management, and regulation of AI. Qualitative template analysis is used to examine sociotechnical sources of risk and resilience, drawing on and elaborating Macrae's (2022, Risk Analysis, 42(9), 1999-2025) SOTEC framework that integrates structural, organizational, technological, epistemic, and cultural sources of risk in AIS. This analysis explores an array of sociotechnical sources of risk associated with the development, deployment, and use of AI in healthcare and identifies an array of sociotechnical patterns of resilience that may counter those risks. In doing so, the SOTEC framework is elaborated and translated to define key sources of both risk and resilience in AIS.

The legal regulation of artificial intelligence security in Ukrainian banking

The need to expand the range of banking services in Ukraine is stipulated with technological progress, the European integration processes and the legal regime of martial law introduced in the country. Under the conditions of war, the need to strengthen the security of banking activities and protect the banking system from the influence of any internal and external factors gains meaning. The topical direction of economic and legal research of scientists today is the possibility to introduce digital technologies with elements of artificial intelligence (AI) into the banking activity in Ukraine to improve its protection. The AI law as an independent branch of the Ukrainian law has not been developed so far. The sources of AI law, its functions, tasks, scope, risks and limits of legal responsibility for prohibited practices of artificial intelligence have not been defined. The purpose of the article is to analyze the theoretical and legal provisions that underpin the regulation of AI application in Ukrainian banking. The comparative legal method made it possible, considering the provisions of the draft law on AI of the European Union, to determine the trends in the development of the legal regulation of AI in Ukraine. Following the study, proposals to the legislation of Ukraine were formulated, which will contribute to the legal regulation of banking activities using digital technologies with elements of AI.

Job Related Uncertainty in the age of Artificial Intelligence and Gig Economy

In this article, we explore the duality of the gig economy enabled by artificial intelligence (AI) in terms of volatility and the promise it offers. We focus primarily on underrepresented and disadvantaged groups of workers who have found a new home, thanks to the gig economy made possible by AI. We examine how their uncertainty is perceived in the existing literature. We then reveal the lack of attention to this group of workers, whose exclusion from the traditional labour market was not problematic in the first place, and who are now portrayed as suffering from poor AI regulation. As such, we expose the hypocrisy of how the development of an AI-enabled gig economy exacerbates job insecurity, but often overlooks the potential of the gig economy to open up opportunities for atypical workers. One problem we have identified is the overrepresentation of atypical workers in the industry. We show that an AI-powered gig economy that does not introduce uncertainty is possible if the sector is effectively regulated. We offer a short roadmap with multi-layered regulatory measures to combat the instability of the artificial gig economy.