AI in Corporate Disclosure: IR Survey Evidence, Legal Risks, and Research Opportunities

Getting AI Right: Introductory Notes on AI & Society

Artificial Intelligence (AI) is rapidly affecting the healthcare system that is more sophisticated. The emergence of AI in Indonesia still gives potential problem on legal risks. Indonesia with its Health Act has actually covered of the technology development, including AI. In fact, in practical aspect, the legal risks coming out from AI malpractice is unclear to tackle. In different circumstances, AI technology has some potential uses which is beneficial for health service development, for instance, data management, drug creation, treatment design, nursing, etc. In this paper, the authors would like to analyze the legal risk possibilities resulting from AI technology in the field of medicine, then it would be synthesized into legal aspects and its legal basis, whether or not the existing AI-related laws should be strengthened in order to tackle the problem occurring from AI malpractice.

Artificial Intelligence (AI) has evolved rapidly, transforming diverse industries and societal functions. This paper provides a comprehensive overview of AI's current landscape, examining its advancements, applications, and ethical challenges. Key trends are explored, including innovations in machine learning and deep learning, AI’s expanding role across industries, and its potential for addressing climate change and sustainability. Furthermore, the paper highlights AI's role in enhancing human-machine collaboration, paving the way for systems that augment rather than replace human capabilities. Predictions for AI’s future are discussed, such as the emergence of artificial general intelligence (AGI), advancements in autonomous systems, the impact of quantum computing on AI, and innovations in AI-specific hardware. The paper also examines ethical and societal challenges, such as privacy, algorithmic bias, and the need for global governance, addressing the urgent call for responsible AI. In light of these trends, the paper emphasizes future research directions, encouraging interdisciplinary collaboration and a focus on explainable, robust, and resilient AI models. This work aims to shed light on the transformative potential of AI while advocating for ethical practices to ensure a positive and sustainable impact on society.

Purpose: The study not only focuses on the legal risks faced by Ontario businesses in cross-border procurement from China but also considers how sustainability practices and environmental regulations impact these risks. As businesses increasingly prioritize green supply chain management and sustainability, understanding how to mitigate legal risks related to environmental laws, ethical sourcing, and the global push for sustainable practices is critical. The paper explores how these factors intertwine with legal considerations, providing a comprehensive view of the risks businesses face when importing goods and meeting consumer expectations for environmentally responsible products. Methodology: The research utilizes methods, including a mix of statutory analysis, case study, and literature review, to analysis the legal risks associated with cross-border procurement from China. This paper reviewed sources from a mix of academic and legal databases, while it gives priority to studies from the last 10-15 years. Findings: This paper identifies three significant risks facing businesses intellectual property infringement, product liability, and lack of jurisdiction and introduces a fourth: sustainability and environmental compliance. This includes navigating green procurement challenges, the environmental impact of goods being imported, and the potential legal consequences for violating international environmental laws or failing to meet sustainability standards. As sustainability becomes a driving factor in procurement, businesses must adapt to evolving green regulations and ethical sourcing practices to avoid legal pitfalls. Unique Contribution to Theory, Practice and Policy: This study extends Siedel’s Six Forces Model to include environmental and sustainability considerations in business legal risks and international procurement. It adds a dimension to Siedel’s theory, validating that understanding not only the legal but also the environmental and ethical challenges facing businesses is crucial for competitive advantage in cross-border procurement. The paper encourages future research in integrating sustainability with legal risk mitigation strategies in international procurement, offering a new framework that balances both sustainability and legal compliance.

To solve the conflicts between the transparency of capital and current restrictive regulations and regulations regarding consumers’ right to claim information and safety, this paper studies the legal risk and prevention path of the transparency of capital due to artificial intelligence. Firstly, the legal risk ways and means of the transparency of capital are constructed by the combined algorithm of SVR, BP, and RNN under the artificial intelligence algorithm, focusing on data tracking before and after the rectification of sharing enterprises with the combined algorithm of BP neural network and RNN to test whether the preventive measures are effectively implemented in place, and then the legal risk prevention path based on the government and enterprise levels is derived. The study concludes that shared travel legal disputes are rising between 2018 and 2022. Among the 10 types of violations sampled for investigation, age information was accessed in violation of the law the most, reaching 53,607,900, and the least in comparison, precise location information was accessed in violation of the law, 1,029,000. After warnings by government departments, the overall violations by enterprises after rectification were on a downward trend, with the incidence of violations controlled between 28.1% and 43.2%. The research on the legal risks of sharing economy in this paper has realistic value and reference significance to the legislation of transparency of capital.

Maximising the Opportunities of Artificial Intelligence for People Living With Cancer

The Delphi Panel investigation of artificial intelligence in investor relations

Investor relations in Poland: An evaluation of the state of affairs from empirical studies

This paper mainly introduces different measures of disclosure quality, pivotal to know what the quality is, what the characteristics of good information quality are and how prior studies measure disclosure quality as to choose the suitable and appropriate approach sheathing for your research. Besides, because different measures have different strengths and weaknesses, and merits and flaws, thus, this paper can let us latch strengths and weaknesses of each approach in order to mitigate these weaknesses. Disclosures can be classified as any deliberate corporate release of financial or nonfinancial, quantitative or qualitative, mandatory or voluntary, formal or informal information. There are different forms of corporate to disclose information to the gullible public, including conference calls, annual or quarterly reports, investor relations, prospectus, press release, management interview and websites (Gibbins, Richardson and Waterhouse, 1990). The annual report is considered as a dominant document in the capital market (Botosan and Plumlee, 2002). A large voluminous prior studies investigate the corporate disclosure issues via annual reports (e.g. Wallace, Naser and Mora, 1994; Meek, Roberts and Gray, 1995; Inchausti, 1997; Botosan, 1997; Ahmed and Courtis, 1999; Depoers, 2000; Hail, 2002; Botosan and Plumlee, 2002; Hope, 2003a; Hope, 2003b; Coy and Dixon, 2004; Abd-Elsalam and Weetman, 2007). For instance, Coy and Dixon (2004) investigate the change in disclosure quality of the annual reports of the New Zealand universities during the period of 1985–2000. Abdelsalam and Weetman (2007) delve deeper in the matter of the annual reports of Egyptian listed companies in the period of 1991–1992 and 1995–1996. However, conference calls and quarterly reports are oft than not considered as more timely disclosures by corporate. Disclosure literature can investigate wide range “hither and thither” issues: determinants of voluntary disclosures, determinants of compliance with and cleavage unto laws, rules and regulations, the use of accounting information by analysts, and the economic repercussions of different types of disclosures (e.g. Botosan, 1997; Sengupta, 1998; Botosan and Plumlee, 2002; Easley, Hvidkjaer, and O’Hara, 2002, 2004; Francis, LaFond, Olsson, and Schipper, 2005). In certain extent, most of these studies must measure the disclosure quality to perform the research. Prior studies offer different measures or proxies such as index, ratings, readability, intricate textual analysis and uses of proxies for disclosure quality. Disclosure quality can be defined in terms of the precision of a Bayesian investor’s beliefs about the firm value after receiving the disclosure (e.g., Diamond and Verrecchia, 1991). As preparers’ perspectives, some studies define the disclosure quality based on the degree of preparer interested bias in disclosures as King (1996) concludes that preparers likely report veridicus information to vamp up their reputation in sophisticated and gauche general users’ perception. Other studies define the disclosure quality based on readers or users’ degree of understanding the contents (Hopkins, 1996). For example, Hopkins (1996) vindicates that different thoroughfares to prepare information would affect the unwary users’ knowledge of accessing and using such information. Therefore, it is relatively difficult for researchers to directly measure the quality for narratives as they are context-sensitive and personal-subjective in most of the cases. This section is to provide a salutary review and carefully but not pedantically discuss different measures of disclosure quality so that it can also provide new insights for future research to measure the CSR disclosure quality.

Subject/topic. Grain production, being a complex and resource-intensive system, requires effective management at all stages of the production cycle. In this article, the subject of research is internal cost control, a critical element for optimizing the use of resources and increasing the economic efficiency of agricultural enterprises. The research topic is the use of artificial intelligence (AI) as a tool that can qualitatively change the processes of internal cost control in grain production. Goals/objectives. The purpose of this article is to develop recommendations for improving the methodology for internal control of costs for the production of grain products using artificial intelligence. As part of the study, the following tasks were set and solved: to analyze the existing methods of internal control of costs in grain production, to identify their shortcomings; determine the possibilities and advantages of using AI to solve internal control tasks; develop a methodology for the use of AI for the analysis, monitoring and forecasting of costs for the production of grain crops. Methodology. The analysis of theoretical and methodological aspects of the organization and implementation of internal control in agricultural organizations is devoted to the works of Shatina E.N., Kozmenkova S.V., Frolova E.B. [1], Azarskaya M.A. [2], Zakirova A.R., Klychova G.S., Ziganshina B.G., Khoruzhiy V.I., Nigi matullina N.N. [3] et al. As a research tool, such general scientific methods as a systematic approach, comparison, a method of systematization and generalization of data were used. The informational basis of this study was the works of domestic and foreign scientists devoted to improving the internal control system. Results. As a result of the study, working documents for internal control of the cost of producing grain crops using AI have been developed, which will optimize the implementation of control measures. Scope of the results. The practical significance of the study lies in the possibility of applying the developed recommendations when conducting internal control of the costs of producing grain crops using artificial intelligence in order to increase the effectiveness of control measures. The implementation of the proposed recommendations on improving the methodological support of internal control of costs for the production of grain crops will make it possible to form an objective conclusion on the results of the audit and provide the management of the agricultural organization with information for making management decisions. Conclusions/significance. Having studied the existing methods of using artificial intelligence in internal cost control, we found that they did not pay enough attention to the systematization of the results of control procedures. In this regard, we have developed working documents of internal control using AI, allowing to optimize the organization and implementation of control measures

Introduction: Clinical research is a key area in which the use of AI in healthcare data seen a significant increase, even though met with great ethical, legal and regulatory challenges. Artificial Intelligence (AI) concerns the ability of algorithms encoded in technology to learn from data, to be able to perform automated tasks without every step in the process being explicitly to be programmed by a human. AI development relies on big data collected from clinical trials to train algorithms, that requires careful consideration of consent, data origin and ethical standards. When data is acquired from third-party sources, transparency about collection methods, geographic origin and anonymization standards becomes critical. While consent forms used in clinical trials can offer clearer terms for data use, ambiguity remains about how this data can be reused for AI purposes after the trial ends. There are very few or no laws on the use of AI especially in developing countries. Also, there are a lot of misconceptions on the global use of AI. Statement of Objectives: Artificial intelligence as an innovative technology has contributed to a shift in paradigm in conducting clinical research. Unfortunately, AI faces ethical, and regulatory challenges especially in limited resource countries where the technology is still to be consolidated. One of the main concerns of AI involves data re-identification, in which anonymized data can potentially be traced back to individuals, especially when linked with other datasets. Data ownership is also a complex and often controversial area within the healthcare sector. AI developers needs to clearly explain the value of data collection to hospitals and cybersecurity teams to ensure that they understand how the data will be secured and used ethically Methodology: The World Health Organization (WHO) recognizes that AI holds great promise for clinical health research and in the practice of medicine, biomedical and pharmaceutical sciences. WHO also recognizes that, to fully maximize the contribution of AI, there is the need to address the ethical, legal and regulatory challenges for the health care systems, practitioners and beneficiaries of medical and public health services. In this study we have pulled data from accessible websites, peered reviewed open-access publications that deal with the ethical and regulatory concerns of AI, that we have discussed in this writeup. We have attempted to place our focus on the development of AI and applications with particular bias in the ethical and regulatory concerns. We have discussed and given an insight on whether AI can advance the interests of patients and communities within the framework of collective effort to design and implement ethically defensible laws and policies and ethically designed AI technologies. Finally, we have investigated the potential serious negative consequences of ethical principles and human rights obligations if they are not prioritized by those who fund, design, regulate or use AI technologies for health research. Results: From our data mining and access to multiple documentations, vital information has been pooled together by a systematic online search to show that AI is contributing significantly in the growth of global clinical research and advancement of medicine. However, we observed many ethical and regulatory challenges that has impacted health research in developing economies. Ethical challenges include AI and human rights, patient’s privacy, safety and liability, informed consent and data ownership, bias and fairness. For the legal and regulatory challenges, we observed issues with data security compliance, data monitoring and maintenance, transparency and accountability, data collection, data storage and use. The role of third-party vendors in AI healthcare solutions and finally AI development and integration into the health systems has also been reviewed. Conclusion: The advancement of AI, coupled with the innovative digital health technology has made a significant contribution to address some challenges in clinical research, within the domain of medicine, biomedical and pharmaceutical products development. Despite the challenging ethical and regulatory challenges AI has impacted significant innovation and technology in clinical research, especially within the domain of drug discovery and development, and clinical trials studies.

To explore the ethical considerations and challenges faced by nursing professionals in integrating artificial intelligence (AI) into patient care. AI's integration into nursing practice enhances clinical decision-making and operational efficiency but raises ethical concerns regarding privacy, accountability, informed consent, and the preservation of human-centered care. A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Thirteen studies were selected from databases including PubMed, Embase, IEEE Xplore, PsycINFO, and CINAHL. Thematic analysis identified key ethical themes related to AI use in nursing. The review highlighted critical ethical challenges, such as data privacy and security, accountability for AI-driven decisions, transparency in AI decision-making, and maintaining the human touch in care. The findings underscore the importance of stakeholder engagement, continuous education for nurses, and robust governance frameworks to guide ethical AI implementation in nursing. The results align with existing literature on AI's ethical complexities in healthcare. Addressing these challenges requires strengthening nursing competencies in AI, advocating for patient-centered AI design, and ensuring that AI integration upholds ethical standards. Although AI offers significant benefits for nursing practice, it also introduces ethical challenges that must be carefully managed. Enhancing nursing education, promoting stakeholder engagement, and developing comprehensive policies are essential for ethically integrating AI into nursing. AI can improve clinical decision-making and efficiency, but nurses must actively preserve humanistic care aspects through ongoing education and involvement in AI governance. Establish ethical frameworks and data protection policies tailored to AI in nursing. Support continuous professional development and allocate resources for the ethical integration of AI in healthcare.

Purpose: an analysis of the impact of artificial intelligence on organisations, with a particular focus on the legal liability associated with the use of AI-based tools. Design/methodology/approach: legal and qualitative analysis of AI’s impact on organizations, focusing on general principles of civil law and regulatory frameworks like the AI Act. The paper adopts a formal-dogmatic method of interpreting legal texts and case-study approach, examining specific instances of AI use, and covers theoretical aspects of liability, data privacy, intellectual property etc. Findings: the analysis reveals that while AI greatly enhances efficiency and innovation, it also introduces complex legal risks, particularly concerning data privacy, liability, and intellectual property. In many cases, existing rules and laws may be adequate to deal with potential infringements using AI. The EU legislation being implemented provides for new specific obligations for AI operators complementing existing spheres of legal liability. Due to the legal uncertainty, organisational managers have to balance efficiency and accountability. Originality/value: an analysis of the impact of artificial intelligence (AI) on the legal liability of organisations, particularly in the context of civil and administrative law (within EU). The paper highlights AI as a key tool in organisations, but also identifies legal risks due to the lack of national legislation specific to AI. It is intended for decision-makers implementing AI within organisations.

The Value of Artificial Intelligence for Healthcare Decision Making—Lessons Learned

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.