Artificial Intelligence Products Research Articles

Building a better lawyer: Experimental evidence that artificial intelligence can increase legal work efficiency

AbstractRapidly improving artificial intelligence (AI) technologies have created opportunities for human–machine cooperation in legal practice. We provide evidence from an experiment with law students (N = 206) on the causal impact of machine assistance on the efficiency of legal task completion in a private law setting with natural language inputs and multidimensional AI outputs. We tested two forms of machine assistance: AI‐generated summaries of legal complaints and AI‐generated text highlighting within those complaints. AI‐generated highlighting reduced task completion time by 30% without any reduction in measured quality indicators compared to no AI assistance. AI‐generated summaries produced no change in performance metrics. AI summaries and AI highlighting together improved efficiency but not as much as AI highlighting alone. Our results show that AI support can dramatically increase the efficiency of legal task completion, but finding the optimal form of AI assistance is a fine‐tuning exercise. Currently, AI‐generated highlighting is not readily available from state‐of‐the‐art, consumer‐facing large language models, but our work suggests that this capability should be prioritized in the development of legal AI products.

Integrating large language models in mental health practice: a qualitative descriptive study based on expert interviews

BackgroundProgress in developing artificial intelligence (AI) products represented by large language models (LLMs) such as OpenAI’s ChatGPT has sparked enthusiasm for their potential use in mental health practice. However, the perspectives on the integration of LLMs within mental health practice remain an underreported topic. Therefore, this study aimed to explore how mental health and AI experts conceptualize LLMs and perceive the use of integrating LLMs into mental health practice.MethodIn February–April 2024, online semi-structured interviews were conducted with 21 experts (12 psychiatrists, 7 mental health nurses, 2 researchers in medical artificial intelligence) from four provinces in China, using snowballing and purposive selection sampling. Respondents’ discussions about their perspectives and expectations of integrating LLMs in mental health were analyzed with conventional content analysis.ResultsFour themes and eleven sub-themes emerged from this study. Firstly, participants discussed the (1) practice and application reform brought by LLMs into mental health (fair access to mental health services, enhancement of patient participation, improvement in work efficiency and quality), and then analyzed the (2) technological-mental health gap (misleading information, lack of professional nuance and depth, user risk). Based on these points, they provided a range of (3) prerequisites for the integration of LLMs in mental health (training and competence, guidelines for use and management, patient engagement and transparency) and expressed their (4) expectations for future developments (reasonable allocation of workload, upgrades and revamps of LLMs).ConclusionThese findings provide valuable insights into integrating LLMs within mental health practice, offering critical guidance for institutions to effectively implement, manage, and optimize these tools, thereby enhancing the quality and accessibility of mental health services.

Article: Exploring Shortcomings in International Trade Law in Light of the Expansion of Artificial Intelligence

The objective of this paper is to explore the shortcomings in international trade law (ITL) in view of the expansion of Artificial Intelligence (AI), and to propose some recommendations. AI is contributing to the development of international trade while giving rise to other concerns, such as nontariffs measures. Addressing the legal challenges posed by the integration of AI into various facets of society, including international trade, is of major importance. In this context, this paper explores a number of provisions of the General Agreement on Tariffs and Trade (GATT) 1994, those of the General Agreement on Trade in Services (GATS), and ends with those of the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS). Finally, the article proposes some recommendations to better take into account the concerns that AI may give rise to in the multilateral trading system. It highlights the need for close partnership between the World Trade Organization (WTO), the International Standardization Organization (ISO) and the International Electrotechnical Commission (IEC) to better maximize the benefits of AI while minimizing the risks in the context of international trade. Also, it highlights the importance of harmonizing regulations on the trade of AI products and AI-driven trading and the need for a change in legal design that goes beyond mere incremental adjustments.

Reaping the rewards with minimal toil: Evaluating the polemics of artificial intelligence in academia and the future of academic writing

The use of Artificial Intelligence in academic writing swings on the edge of revolution and obliteration, where the promise of unparalleled efficiency clashes with the specter of intellectual dilution, which challenges the essence of academic rigor and originality. This paper interrogates the ways in which the two extremes of innovation and traditional writing methods in academia can be integrated. As such, the paper argues that it is essential to incorporate new technologies and modern AI tools with critical and historical aspects of academic culture rather than replacing them. In essence, AI should only augment what researchers are able to do, not replace them. As a result, it is important to set specific guidelines and standards in order to deal with issues, of authorship, data collection, controlled storage, and validation of artificial intelligence products. Lastly, the paper recommends the necessity of training and education about AI tools for academics and scientists in institutions of higher learning. The academic community should seek to implement policies and strategies that allow for the use of AI technologies in the execution of scholarly activities without compromising the quality of academic writing.

Integrating Artificial Intelligence into Service Innovation, Business Development, and Legal Compliance: Insights from the Hainan Free Trade Port Era

This research aims to inspect the application of Artificial Intelligence (AI) in product and service innovation from the perspective of the Hainan Free Trade Port (HFTP) and its relationship with corporate transformation, legal compliance, and regulatory oversight. Being critical to the fourth industrial revolution, digital business and international cooperation, technology propels enterprises across various industries to transition from traditional models to intelligent and service-oriented ones. It also elucidates the theoretical foundations of AI products, the digital economy, and service innovation. It can be used to analyzes the challenges enterprises face in the HFTP while implementing AI technology, including funding, technology, management, operations, corporate culture, and innovative concepts. Based on the proposed research methodology, three hypotheses can be formulated. Hypothesis 1 states that the HFTP could facilitate enterprise transformation by applying supportive policies. Hypothesis 2 state that domestic laws and international agreements are urgently needed due to the legal risks arising from artificial intelligence. Hypothesis 3 state that HFTP enterprises comply with these laws while systemically assuring, in theory, and practice, the legal risks of artificial intelligence and its implications for legal regulation, which is a significant aspect of research, addressing legal risks related to data privacy, security, and algorithmic bias with many strategies being proposed. This shows how AI technology can change businesses in the HFTP, demonstrating the application of AI technology in the transformation of enterprises in the HFTP and the various risks they may encounter, providing valuable references for other enterprises regarding the practical significance of AI product and service innovation in the HFTP, and emphasizing the importance of international cooperation and legal instruction.

Deployment of Artificial Intelligence in Radiology: Strategies for Success.

Radiology, as a highly technical and information-rich medical specialty, is well-suited for artificial intelligence (AI) product development, and many FDA-cleared AI medical devices are authorized for uses within the specialty. In this Clinical Perspective, we discuss the deployment of AI tools in radiology, exploring regulatory processes, the need for transparency, and other practical challenges. We further highlight the importance of rigorous validation, real-world testing, seamless workflow integration, and end-user education. We emphasize the role for continuous feedback and robust monitoring processes, to guide AI tools' adaptation and help ensure sustained performance. Traditional standalone and alternative platform-based approaches to radiology AI implementation are considered. The presented strategies will help achieve successful deployment and fully realize AI's potential benefits in radiology.

Living in the Digital Age: AI Technology and the Changing Lifestyle of the Elderly

As technology advances, diverse artificial intelligence products are invented and put into use. While dramatically improving the quality of human life, digital technologies are also changing the lifestyles of different groups to varying degrees. Focusing on the elderly population, this study utilizes a combination of questionnaire, interview, and documentary research methods in hopes of collecting information in a broad and comprehensive manner. The study mainly focuses on how AI technologies in the digital age are changing the lifestyles of older people and the potential factors that lead to such changes. The study reveals that the digital age has a dual impact on the lives of older people. Thanks to the intelligence and simplicity of AI technology, older people’s lives have become much more efficient and convenient. However, the expensive price of AI devices and products, which also keeps some older people from enjoying the convenience of technological inventions, prevents them from integrating into the digital society. Among many lifestyles, changes in home-based lifestyles are the areas of greatest change in the lifestyles of the elderly in the digital age. These changes are mainly influenced by social, technological and personal factors. The present study expresses ample social concerns for the elderly population. It also conducive to helping older people fully integrate into a smart and convenient life and equally enjoy the dividends of the digital age.

The Social Identity of the Robot Pet: A multi-dimensional perspective

With the invention and application of artificial intelligence products into modern society, robot pets have become one of the important products. At present, along with the gradual integration of robot pets into human life and becoming people’s intimate companions, more and more people begin to pay attention to the social identity of robot pets. In order to clarify the social identity of robotic pets in modern society, this study used questionnaires and interviews to collect the acceptance attitudes and perspectives of residents from the Yangtze River Delta region toward robotic pets as companions. It is found that there is diversity in public acceptance of robotic pets, and that health, emotional needs, and the function of the robots are important factors influencing people’s choice of robotic pets. Regarding the social status of robotic pets, some people believe that robotic pets should be regarded as an integral part of society. While others believe they lack socio-emotional attributes and should not have a social identity. In the future, pet robots will move toward personalization, multifunctionality, or cross-industry cooperation, where their social identity is expected to be strengthened. In conclusion, this study organizes the social cognition of robotic pets, gives an empirical basis for the study of the social identity of robotic pets, and provides guidance for the future direction of AI products.

The hidden abode of artificial intelligence production: Stretching the limits of artificial intelligence ethics and critique

The present article aims to discuss the possibility of including the sphere of artificial intelligence production within the domain of artificial intelligence ethics and investigate its moral implications. In the first section, the role of human labour in the artificial intelligence production processes is considered, with particular reference to the distinction between high-skilled and low-skilled jobs, their differential distribution in the production process itself, and the labour conditions of ghost workers, in order to analyse the main ethical issues emerging within the field. In the second section, some aspects of the existing critical literature concerning artificial intelligence and labour are discussed, focusing on Marxist and decolonial scholarship and more precisely on its lack of consideration of the global value chain through which artificial intelligence AI production processes are structured. Finally, the possibility and limits of an ethics of artificial intelligence production are reconsidered by assuming the centrality of workers’ struggles and agency along artificial intelligence's global value chain.

Pitfalls in Interpretive Applications of Artificial Intelligence in Radiology.

Interpretive artificial intelligence (AI) tools are poised to change the future of radiology. However, certain pitfalls may pose particular challenges for optimal AI interpretative performance. These include anatomic variants, age-related changes, postoperative changes, medical devices, image artifacts, lack of integration of prior and concurrent imaging examinations and clinical information, and the satisfaction-of-search effect. Model training and development should account for such pitfalls to minimize errors and optimize interpretation accuracy. More broadly, AI algorithms should be exposed to diverse and complex training datasets to yield a holistic interpretation that considers all relevant information beyond the individual examination. Successful clinical deployment of AI tools will require that radiologist end users recognize these pitfalls and other limitations of the available models. Furthermore, developers should incorporate explainable AI techniques (e.g., heat maps) into their tools, to improve radiologists' understanding of model outputs and to enable radiologists to provide feedback for guiding continuous learning and iterative refinement. In this article, we provide an overview of common pitfalls that radiologists may encounter when using interpretive AI products in daily practice. We present how such pitfalls lead to AI errors and offer potential strategies that AI developers may use for their mitigation.

The role of institutional innovation and artificial intelligence in the corporate eco-innovation - a case study of high-tech enterprises in Liaoning province of China

This research investigates the intricate interplay of institutional factors and technological adoption in the context of corporate ecological innovation. Grounded in the New Institutionalism Theory and the Technology-Organization-Environment (TOE) framework, the study explores how formal and informal institutional innovations shape the behavior and capabilities of companies engaged in ecological innovation in Liaoning Province, China. Furthermore, the research examines the role of Artificial Intelligence (AI) in promoting ecological innovation. The study classifies AI into three categories: data analytics and predictive AI, automation and intelligent production AI, and customer service and user experience AI. These dimensions are investigated in terms of their influence on ecological innovation, revealing how each element contributes to enhancing companies' capabilities in addressing ecological challenges. The research proposes hypotheses regarding the positive influence of these AI dimensions on ecological innovation outcomes. The findings from this research contribute to the education of the intricate dynamics between institutional innovations, technological adoption, and corporate ecological innovation. The insights garnered are crucial for policymakers, businesses, and researchers striving to cultivate sustainable practices and innovation in ’the contemporary economic and environmental milieu.

Erosion and Remodeling: New Alienation of User Subjects Led by Intelligent Algorithms

As the core technological means of today's artificial intelligence products, intelligent algorithms have become an indispensable tool for human development. They have led the progress of the intelligent era, fully demonstrating the powerful charm and infinite possibilities of technology, and depicting a hopeful intelligent future. However, while bringing convenience and imagination to users, it also makes them objects dominated by intelligent technology, gradually losing their independence and creativity as humans, losing their ability to criticize and think deeply, and causing a series of inevitable alienation problems that cannot be ignored. This article uses qualitative research methods to conduct in-depth interviews with respondents, extract their attitudes and opinions towards one of the artificial intelligence technologies - algorithms, and organize and analyze them. The phenomenon of "new alienation" brought about by reshaping algorithms is based on users' real thinking and more accurate needs, breaking the constraints of intelligence and adding benefits to the development of algorithms.

Competing Legal Futures – “Commodification Bets” All the Way From Personal Data to AI

Abstract This Article explores the implications of Artificial Intelligence (AI)-driven innovation across sectors, highlighting the resulting legal uncertainties. Despite the transformative influence of AI in healthcare, retail, finance and more, regulatory responses to these developments are often contradictory, contributing to the opacity of the legal underpinnings of AI business. Our Article notes the common trend of commercializing AI tools amidst legal uncertainty, using innovative contractual solutions to secure claims. Over time, these innovations trigger overlooked legal conflicts, sometimes leading to outright bans on AI products due to negative impacts on some fundamental rights and democratic Governance. The core argument of our Article is that an over-reliance on co-regulatory strategies, such as those proposed by the European AI Act, exacerbates legal instability in emerging technological markets. This panorama creates an ’extended legal present’ when alternative legal expectations coexist, thus causing economic and political uncertainty that may elicit legal instability in the future. The concept of ’competing legal futures’ is introduced to illustrate how economic actors must bet on a legal future in the absence of guarantees that this future will materialize. To help analyze this complex narrative, we propose a theoretical framework for understanding legal, technological, and economic dynamics, highlighting anomalies in market exchanges within the co-regulatory model. Despite the focus on European developments, the practical and theoretical implications extend beyond the EU, making the Article relevant to a broader understanding of the legal-economic challenges posed by AI and digital innovation. We conclude by arguing for a course correction, proposing institutional diversification for resilient governance of legal innovation under uncertainty.

Nursing Education in the Era of ChatGPT: Implications and Opportunities

ChatGPT is a generative pre-trained transformer language model developed by OpenAI. ChatGPT is designed to generate conversational, human-like text responses and has been trained with diverse textual data to provide coherent and contextually relevant answers to user queries. This article highlights the advantages of using ChatGPT in nursing education, emphasizing the benefit of enhancing teaching and learning experiences, supporting critical thinking, and providing personalized learning opportunities. The article also discusses barriers to use of ChatGPT, such as limited access to technology and concerns about information accuracy. Discussion of ethical concerns related to AI, including biases, data privacy, transparency, and other psychosocial concerns is also included, along with an overview of efforts to address these potential dilemmas. The article concludes by providing recommendations for best practices to responsibly integrate artificial intelligence products into nursing education, emphasizing the need for educators to become informed about AI technologies and guide cautious and ethical use of AI within nursing education. The work herein underscores the importance of transforming nursing curricula to equip future nurses with digital and data literacy skills necessary for working in today’s increasingly AI-driven environments.

Survival Analysis of Penile Cancer Patients Using Cox-Proportional Hazard and GPT-4

Today, in 2024, the evolution of the evolution of artificial intelligence (AI) is very impressive. An AI product that continues to develop is ChatGPT. The threat of ChatGPT to some job professions is real. Beside that, ChatGPT also helps many professions make their work easier. In this research, we analyze the survival analysis of penile cancer patients. The design of this research is mixed-method (quantitative-qualitative). The quantitative and qualitative methods that were used are Cox-proportional hazards and GPT-4, respectively. Based on quantitative analysis, it can be concluded that the Survival and Hazard model for study penile cancer are and , respectively. The predicted chances of survival and death for the patient in stage 2, 14 days after surgery, are 40% and 60%, respectively. Based on qualitative analysis, GPT-4 cannot quantitatively obtain the cox proportional hazard number. However, even so, GPT4 can read the cox proportional hazard output image, providing Python and R survival analysis coding quite perfectly. Thus, the qualitative method's conclusion is that GPT-4 can greatly assist data analysts in their analysis of survival data.

Visible Artificial Intelligence: Exploring the Enhancement of Chatgpt and Film Script Creation

As a joint link of artificial intelligence products, the emergence of ChatGPT triggered a global debate, and attracted the attention of practitioners all over the world. Meanwhile, multiple film and television production fields such as computer graphics processing, digital virtual shooting, CG special effects production, have embedded automatic content generation factors. However, artificial intelligence is also facing legal dilemmas and ethical constraints, researchers need to compare and analyze its advantages and problems, systematically consider the strengths and impacts, limitations and problems of AIGC, and provide reference and practical guidance for AI to better promote itself.

Risks of AI Applications Used in Higher Education

As artificial intelligence (AI) tools become more widely used in higher education, we must pay attention to the risks that can emerge. AI projects, whether applied in classroom learning or used for decision-making regarding admissions, financial aid allocation, or hiring, must include attention to governance and compliance issues, regardless of the project’s scope and scale. Concerns highlighted in this work include transparency, user privacy, data confidentiality, data integrity, and system availability, however, we note that this is a non-exhaustive list of risks. In this paper, risk assessment is defined, and two examples of risk management frameworks, namely the United States National Institute of Standards and Technology Artificial Intelligence Risk Management Framework and the non-profit humanitarian effort ForHumanity’s Independent Audit of AI, Algorithmic, and Autonomous Systems are briefly described. We identify characteristics of AI applications that need to be assessed for vulnerabilities they may present, such as bias and discrimination. This paper aims to facilitate discussion among stakeholders about the risks that may be encountered from using AI in higher education, as well as to suggest ways developers, decision-makers, and users can mitigate these risks. Much discussion and published literature has focused on risk management frameworks designed for large organizations or enterprises or frameworks that do not consider risks specific to AI. We hope that decision-makers carefully consider the risks, perform due diligence when implementing AI applications, and create a plan for mitigating the risks. This research supports e-learning practice because students and faculty are embracing AI applications. Leaders and decision-makers in higher education need to be proactive in protecting their varied stakeholders. The paper asks what risks may be encountered by institutions of higher education when using AI tools and products in the classroom and for various aspects of decision-making and if published frameworks can mitigate these risks.

“Trustworthy AI” Cannot Be Trusted: A Virtue Jurisprudence-Based Approach to Analyse Who Is Responsible for AI Errors

Erroneous results generated by artificial intelligence (AI) have opened up new questions of who is responsible for AI errors in legal scholarship. I support the prevailing academic view that human subjects should be held responsible for AI errors. However, I argue that the underlying reason is not pertained to the reliability of AI, but rather the inability of humans to establish a trusting relationship with AI. The term ‘Trustworthy AI’ is just a metaphor, which presents a sense of trust; AI itself is not trustworthy. The first section outlines the academic debate on the responsibility of AI. It contends that the perspective of these debates has shifted from the characteristics of AI, such as autonomy and explainability, to a human-centred perspective, which is how humans should develop AI. The assumption of responsibility depends on the existence of a trust relationship because when people believe that an individual can fulfil his or her responsibilities, they are willing to hand over power, resources or tasks to that individual. It applies a virtue jurisprudence-based approach to explain why humans cannot establish a trust relationship with AI To establish such a relationship, one subject must indicate to the other that its behaviour is based on specific moral motivation and that it can be held moral responsibility. Nevertheless, AI lacks moral motivation and moral responsibility. The third section reconsiders the scope of responsible subjects for AI errors. It posits that accountability should be limited to the individuals who are direct beneficiaries of the AI product. Finally, it argues that the scope of responsibility for AI errors should be disparate pursuant to the risk level of the AI. For high-risk AI, responsible subjects must fulfil both the obligations under the AI Act and the obligation to provide technical authentication. Keywords: AI Law, Trustworthy AI, AI Act, Legal Responsibility, Virtue Jurisprudence

AI-driven capital-skill complementarity: Implications for skill premiums and labor mobility

To explain the newly discovered coevolution of capital and labor structures, this study presents a modified capital-skill complementarity hypothesis within the framework of structural transformation. We propose that artificial intelligence (AI) and skilled labor exhibit relative complementarity. Specifically, advancements in AI services or AI-enhanced technologies incentivize the mobilization of skilled labor across different industry sectors. The direction of this labor movement is contingent upon variations across industry sectors, including AI output elasticity and the substitutability between AI and conventional production methods. This structural transformation process also induces fluctuations in skill premiums.

From attention economy to cognitive lock-ins

The economic logic of the attention economy is frequently used to critique and respond to the dangers of unfettered technological expansion, including nascent platforms and products powered by generative artificial intelligence. This commentary warns that while large parts of the internet have been financed through such business models, there is no guarantee that emerging generative artificial intelligence products will be commercialized in this way too. Instead, I argue, we must look beyond the attention economy to predict the future of monetization of an industry already mired in anti-competitive practices. Using popular large language models such as OpenAI’s ChatGPT as a case, I discuss how some platforms are developing computational dependencies between technology and their users. I propose the term ‘cognitive lock-in’ to help us unpack the implications of such technological dependencies, and redirect the study of this nascent business model.