Regulation Of Artificial Intelligence Research Articles

REGULATORY FRAMEWORKS FOR ARTIFICIAL INTELLIGENCE IN LAW: ENSURING ACCOUNTABILITY AND FAIRNESS

In recent years, the integration of artificial intelligence (AI) in the legal sector has transformed the way legal services are delivered, enhancing efficiency, accuracy, and accessibility. However, the rapid advancement of AI technology in law also raises significant concerns regarding accountability and fairness. This article explores the regulatory frameworks aimed at addressing these concerns and ensuring that AI systems in the legal domain operate ethically and responsibly. Beginning with an introduction to the role of AI in law and its implications, the article navigates through the complex landscape of AI regulation, highlighting global perspectives, key regulatory bodies, and existing laws relevant to AI in legal practice. It then delves into the principles and mechanisms essential for ensuring accountability in AI systems, including transparency, explainability, and data governance. Furthermore, the article investigates strategies for achieving fairness in AI-powered legal systems, addressing issues such as bias, discrimination, and the need for fairness metrics and evaluation methods. It explores governance mechanisms necessary for effective regulation, emphasizing stakeholder engagement, compliance, and enforcement strategies. Drawing insights from case studies and best practices, the article offers valuable lessons and recommendations for policymakers, practitioners, and stakeholders involved in shaping the future of AI regulation in the legal sector. In conclusion, it underscores the importance of continuous evaluation and adaptation to keep pace with the evolving landscape of AI technology and its impact on the legal profession.

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.

"BIG DATA SOHASIDA SUNʼIY INTELLEKTNING HUQUQ VA MAJBURIYATLARI: MUAMMOLAR HAMDA YECHIMLAR "

"This article is devoted to analyzing problems and potential solutions in the regulation of the rights and responsibilities of artificial intelligence (AI) in the context of the processing and analysis of Big Data. We explore the legal and ethical aspects of accountability, transparency, non-discrimination, and protection of personal data in the use of AI. The research examines existing legal frameworks, and we propose measures that have the potential to improve them significantly, including the development of specific legislation, the establishment of independent audit mechanisms, the establishment of transparency and accountability requirements, and the promotion of international cooperation. Particular attention is paid to the role of transparency and accountability principles in AI regulation. The features of regulation of AI and Big Data in Uzbekistan are analyzed. It is concluded that with these promising measures, it is necessary to create an effective and balanced regulatory system that ensures the development of innovative technologies and the protection of human rights in the digital age. "

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?

Regulation of Artificial Intelligence in Russia: Eclecticism of Approaches and Actors

Many observers expect artificial intelligence (AI) to become one of the most important technologies of the 21st century, whose development will be a decisive factor in shaping the future global technological balance of power. A practical confirmation of this statement is the fact that today an increasing number of states are striving to accelerate their national AI development programmes. Russia is one of them, although it is generally not perceived in the West as a potential leader in this emerging competition. At the same time, Russia’s demonstrated ability to build its own successful national IT ecosystem is attracting increased international interest in its national AI development efforts. The purpose of this paper is to outline the contours of these efforts, and to trace the evolution of the regulatory framework and its underlying principles. The authors draw the following conclusions. Russia is developing a flexible and science-based approach to AI and its potential humanitarian impacts, combining its own historical experience with international practices. Strategically, Russia sees the development of national AI as an important means of advancing its economic and technological development in a way that benefits its citizens and businesses, as well as preventing a possible economic and technological slowdown. Significant progress has been made on legal and ethical issues. The focus of Russian efforts in this area is more on soft law norms and self-regulation rather than on prohibitive legislative norms. This approach leaves a lot of room for the development of AI and reduces the possibility of its premature bureaucratisation, which could hinder innovation in the field. There are certain similarities between the Russian and European approaches to AI development and regulation. Human security concerns, data protection and a risk-based approach to the development of AI technologies are prioritised by the state and supranational institutions (respectively) as the main actors responsible for safeguarding the well-being, interests and rights of the citizens.

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.

НОРМАТИВНЕ РЕГУЛЮВАННЯ ТЕХНОЛОГІЙ ШІ ТА ВПЛИВ НА БІЗНЕС

The functioning of business today is unimaginable without the use of artificial intelligence (AI) tools. The issue of regulating AI technologies in Ukraine and the impact of such algorithms on the development of business structures is relevant. The impact of AI on the digitalization of business can be observed in the following: automation of routine tasks; the ability to process large amounts of data and provide analytical reports; providing clients with personalized offers; the ability to predict future market trends. In Ukraine, AI regulation procedures began in December 2020 with the approval of the «Concept of Artificial Intelligence Development». The next step in regulation was the presentation of the «Roadmap for AI Regulation» document by the Ministry of Digital Transformation in October 2023. The document aimed to help Ukrainian businesses prepare for the adoption of a law that would be analogous to the European Union's EU AI Act. The adoption of the law was preceded by the publication of the European Strategy for AI in 2018, from which the Recommendations for Trustworthy AI were formed in 2019, and the Assessment of AI Trustworthiness in 2020. In parallel, the first Coordinated Plan on AI was published in 2018 as a joint commitment by EU member states. In February 2020, the European Commission approved a White Paper on AI. In April 2021, the European Commission began work on a full-fledged Regulation, which was finalized in March 2024 with the adoption of the EU AI Act, approved by the EU Council on May 21. The latest government document is the White Paper on AI Regulation in Ukraine: Vision of the Ministry of Digital Transformation. The main goals of the White Paper are: supporting businesses to increase competitiveness, protecting human rights from possible violations, and adapting Ukrainian legislation to EU standards. The White Paper describes the following prerequisites for developing a BOTTOM-UP approach: balancing interests; prudence and timeliness in implementing regulation; challenges in AI regulation; European integration «Brussels effect»; service function and product orientation. The starting tools are: regulatory sandbox, methodology for assessing the impact of AI on human rights, AI labeling tools, soft law tools (voluntary codes of conduct, general and sectoral recommendations), creation of a responsible AI web portal, and the final version of the White Paper. The logical continuation of Phase I is Phase II BOTTOM-UP – the adoption of the law analogous to the EU AI Regulation and the implementation of mandatory regulation through implementation. The White Paper proposes a path for AI regulation in Ukraine.

Legal aspects of generative artificial intelligence and large language models in examinations and theses.

The high performance of generative artificial intelligence (AI) and large language models (LLM) in examination contexts has triggered an intense debate about their applications, effects and risks. What legal aspects need to be considered when using LLM in teaching and assessment? What possibilities do language models offer? Statutes and laws are used to assess the use of LLM: - University statutes, state higher education laws, licensing regulations for doctors - Copyright Act (UrhG) - General Data Protection Regulation (DGPR) - AI Regulation (EU AI Act) LLM and AI offer opportunities but require clear university frameworks. These should define legitimate uses and areas where use is prohibited. Cheating and plagiarism violate good scientific practice and copyright laws. Cheating is difficult to detect. Plagiarism by AI is possible. Users of the products are responsible. LLM are effective tools for generating exam questions. Nevertheless, careful review is necessary as even apparently high-quality products may contain errors. However, the risk of copyright infringement with AI-generated exam questions is low, as copyright law allows up to 15% of protected works to be used for teaching and exams. The grading of exam content is subject to higher education laws and regulations and the GDPR. Exclusively computer-based assessment without human review is not permitted. For high-risk applications in education, the EU's AI Regulation will apply in the future. When dealing with LLM in assessments, evaluation criteria for existing assessments can be adapted, as can assessment programmes, e.g. to reduce the motivation to cheat. LLM can also become the subject of the examination themselves. Teachers should undergo further training in AI and consider LLM as an addition.

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.