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Articoli di riviste sul tema "Generative AI Risks"

Autore: Grafiati

Pubblicato: 21 dicembre 2024

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1

El-Hadi, Mohamed. "Generative AI Poses Security Risks." مجلة الجمعية المصرية لنظم المعلومات وتکنولوجيا الحاسبات 34, no. 34 (2024): 72–73. http://dx.doi.org/10.21608/jstc.2024.338476.

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Hu, Fangfei, Shiyuan Liu, Xinrui Cheng, Pengyou Guo, and Mengqi Yu. "Risks of Generative Artificial Intelligence and Multi-Tool Governance." Academic Journal of Management and Social Sciences 9, no. 2 (2024): 88–93. http://dx.doi.org/10.54097/stvem930.

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While generative AI, represented by ChatGPT, brings a technological revolution and convenience to life, it may raise a series of social and legal risks, mainly including violation of personal privacy and data security issues, infringement of intellectual property rights, generation of misleading and false content, and exacerbation of discrimination and prejudice. However, the traditional AI governance paradigm oriented towards conventional AI may not be adequately adapted to generative AI with generalized potential and based on big models. In order to encourage the innovative development of ge

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3

Moon, Su-Ji. "Effects of Perception of Potential Risk in Generative AI on Attitudes and Intention to Use." International Journal on Advanced Science, Engineering and Information Technology 14, no. 5 (2024): 1748–55. http://dx.doi.org/10.18517/ijaseit.14.5.20445.

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Generative artificial intelligence (AI) is rapidly advancing, offering numerous benefits to society while presenting unforeseen potential risks. This study aims to identify these potential risks through a comprehensive literature review and investigate how user’s perceptions of risk factors influence their attitudes and intentions to use generative AI technologies. Specifically, we examined the impact of four key risk factors: fake news generation, trust, bias, and privacy concerns. Our analysis of data collected from experienced generative AI users yielded several significant findings: First,

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Campbell, Mark, and Mlađan Jovanović. "Disinfecting AI: Mitigating Generative AI’s Top Risks." Computer 57, no. 5 (2024): 111–16. http://dx.doi.org/10.1109/mc.2024.3374433.

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5

Siyed, Zahm. "Generative AI Increases Cybersecurity Risks for Seniors." Computer and Information Science 17, no. 2 (2024): 39. http://dx.doi.org/10.5539/cis.v17n2p39.

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We evaluate how generative AI exacerbates the cyber risks faced by senior citizens. We assess the risk that powerful LLMs can easily be misconfigured to serve a malicious purpose, and that platforms such as HackGPT or WormGPT can facilitate low-skilled script kiddies to replicate the effectiveness of high-skilled threat actors. We surveyed 85 seniors and found that the combination of loneliness and low cyber literacy places 87% of them at high risk of being hacked. Our survey further revealed that 67% of seniors have already been exposed to potentially exploitable digital intrusions and only 2

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Sun, Hanpu. "Risks and Legal Governance of Generative Artificial Intelligence." International Journal of Social Sciences and Public Administration 4, no. 1 (2024): 306–14. http://dx.doi.org/10.62051/ijsspa.v4n1.35.

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Generative Artificial Intelligence (AI) represents a significant advancement in the field of artificial intelligence, characterized by its ability to autonomously generate original content by learning from existing data. Unlike traditional decision-based AI, which primarily aids in decision-making by analyzing data, generative AI can create new texts, images, music, and more, showcasing its immense potential across various domains. However, this technology also presents substantial risks, including data security threats, privacy violations, algorithmic biases, and the dissemination of false in

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Mortensen, Søren F. "Generative AI in securities services." Journal of Securities Operations & Custody 16, no. 4 (2024): 302. http://dx.doi.org/10.69554/zlcg3875.

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Generative AI (GenAI) is a technology that, since the launch of ChatGPT in November 2022, has taken the world by storm. While a lot of the conversation around GenAI is hype, there are some real applications of this technology that can bring real value to businesses. There are, however, risks in applying this technology blindly that sometimes can outweigh the value it brings. This paper discusses the potential applicability of GenAI to the processes in post-trade and what impact it could have on financial institutions and their ability to meet challenges in the market, such as T+1. We also disc

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8

Wang, Mingzheng. "Generative AI: A New Challenge for Cybersecurity." Journal of Computer Science and Technology Studies 6, no. 2 (2024): 13–18. http://dx.doi.org/10.32996/jcsts.2024.6.2.3.

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The rapid development of Generative Artificial Intelligence (GAI) technology has shown tremendous potential in various fields, such as image generation, text generation, and video generation, and it has been widely applied in various industries. However, GAI also brings new risks and challenges to cybersecurity. This paper analyzes the application status of GAI technology in the field of cybersecurity and discusses the risks and challenges it brings, including data security risks, scientific and technological ethics and moral challenges, Artificial Intelligence (AI) fraud, and threats from cyb

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9

Gabriel, Sonja. "Generative AI in Writing Workshops: A Path to AI Literacy." International Conference on AI Research 4, no. 1 (2024): 126–32. https://doi.org/10.34190/icair.4.1.3022.

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The widespread use of generative AI tools which can support or even take over several part of the writing process has sparked many discussions about integrity, AI literacy and changes to academic writing processes. This paper explores the impact of generative artificial intelligence (AI) tools on the academic writing pro-cess, drawing on data from a writing workshop and interviews with university students of a university teacher college in Austria. Despite the widespread assumption that generative AI, such as ChatGPT, is widely used by students to support their academic tasks, initial findings

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Tong, Yifeng. "Research on Criminal Risk Analysis and Governance Mechanism of Generative Artificial Intelligence such as ChatGPT." Studies in Law and Justice 2, no. 2 (2023): 85–94. http://dx.doi.org/10.56397/slj.2023.06.13.

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The launch of ChatGPT marks a breakthrough in the development of generative artificial intelligence (generative AI) technology, which is based on the collection and learning of big data as its core operating mechanism. Now the generative AI has the characteristics of high intelligence and high generalization and thus has led to various criminal risks. The current criminal law norms are inadequate in terms of the attribution system and crime norms for the criminal risks caused by the generative AI technologies. Thus, we should clarify the types of risks and governance challenges of generative A

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Andrei, Andreia Gabriela, Mara Mațcu-Zaharia, and Dragoș Florentin Mariciuc. "Ready to Grip AI's Potential? Insights from an Exploratory Study on Perceptions of Human-AI Collaboration." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 2 (2024): 01–22. http://dx.doi.org/10.18662/brain/15.2/560.

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One of the emerging technologies arising with Industry 4.0 is generative artificial intelligence (AI). Despite its disruptive nature and controversies, the effective and ethical use of AI is increasingly preoccupying organizations of all sizes as well as their employees. Focusing on generative AI, this paper presents findings from a qualitative study that provides insights into how Generation Z, the newest workforce, perceives human-AI collaboration. Based on in-depth interviews and a micro-meso-macro approach, the study reveals a dual perspective. Participants recognized the advantages AI bri

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柴, 懿庭. "Generative AI Data Collection Risks and Regulatory Paths." Advances in Social Sciences 13, no. 08 (2024): 763–69. http://dx.doi.org/10.12677/ass.2024.138761.

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Liu, Xiaozhong, Yu‐Ru Lin, Zhuoren Jiang, and Qunfang Wu. "Social Risks in the Era of Generative AI." Proceedings of the Association for Information Science and Technology 61, no. 1 (2024): 790–94. http://dx.doi.org/10.1002/pra2.1103.

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ABSTRACTGenerative AI (GAI) technologies have demonstrated human‐level performance on a vast spectrum of tasks. However, recent studies have also delved into the potential threats and vulnerabilities posed by GAI, particularly as they become increasingly prevalent in sensitive domains such as elections and education. Their use in politics raises concerns about manipulation and misinformation. Further exploration is imperative to comprehend the social risks associated with GAI across diverse societal contexts. In this panel, we aim to dissect the impact and risks posed by GAI on our social fabr

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Feretzakis, Georgios, Konstantinos Papaspyridis, Aris Gkoulalas-Divanis, and Vassilios S. Verykios. "Privacy-Preserving Techniques in Generative AI and Large Language Models: A Narrative Review." Information 15, no. 11 (2024): 697. http://dx.doi.org/10.3390/info15110697.

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Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provides a comprehensive overview of privacy-preserving techniques aimed at safeguarding data privacy in generative AI, such as differential privacy (DP), federated learning (FL), homomorphic encryption (HE), and secure multi-party computation (SMPC). These techniques mitigate risks like model inversion, data leakage, and membership inference attacks, whi

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Kwon, Jungin. "Enhancing ethical awareness through generative AI literacy: A study on user engagement and competence." Edelweiss Applied Science and Technology 8, no. 6 (2024): 4136–45. http://dx.doi.org/10.55214/25768484.v8i6.2904.

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Generative artificial intelligence (AI) enables users to quickly and easily create desired outputs. However, it poses potential risks of social disruption due to biases in the data used to generate such outputs. This study examines whether providing literacy-based guidance on the risks and cautionary aspects of generative AI at the point of user acceptance of its outputs can enhance the user's ethical competence. For the study, participants were divided into an experimental group and a control group. The experimental group received warnings about the risks and considerations of accepting AI-ge

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Zhou, Tong, and Mohamad Rizal Abd Rahman. "Legal Perspective on the Risk of Copyright Infringement by AI-Generated Contents in China." JURNAL UNDANG-UNDANG DAN MASYARAKAT 34, no. 2 (2024): 141–53. http://dx.doi.org/10.17576/juum-2024-3402-10.

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The swift progress of Artificial Intelligence (AI) technology is driving advancements in the internet and cultural sectors, but it also poses challenges to traditional laws, especially copyright law. This article examines the legal complexities associated with AI-generated products and copyright infringement risks in the Chinese internet context. By combining the Copyright Law of the People's Republic of China and analyzing relevant judicial dispute cases in China, this study explores generative artificial intelligence models for generating creative content such as text, images, and music, and

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Nahla, Febri, and Anis Masruri. "Analysis of the Impact of Artificial Intelligence on Information-Seeking Behavior." JPUA: Jurnal Perpustakaan Universitas Airlangga: Media Informasi dan Komunikasi Kepustakawanan 14, no. 2 (2024): 69–75. https://doi.org/10.20473/jpua.v14i2.2024.69-75.

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Background of the study: The need for information in the era of globalization is rapidly growing, causing users to strategize to obtain information effectively. One of the strategies used in information retrieval is generative AI applications. Purpose: This study aims to discuss generative AI applications used in information retrieval and the impact of generative AI usage on information-seeking behavior. Methods: Library research is used to collect data from various relevant literature sources. Finding: The use of generative AI in information retrieval provides positive impacts, such as speedi

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Lasker, Archit. "EXPLORING ETHICAL CONSIDERATIONS IN GENERATIVE AI." International Journal of Advanced Research 12, no. 04 (2024): 531–35. http://dx.doi.org/10.21474/ijar01/18578.

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Generative AI, which encompasses a range of technologies such as generative adversarial networks (GANs), language models, and image generators, has shown remarkable progress in recent years. These technologies have the potential to revolutionize various fields, from art and entertainment to healthcare and education. However, along with these advancements come ethical considerations that must be carefully addressed. This research paper examines the ethical challenges posed by generative AI, including issues related to bias, privacy, misinformation, and intellectual property. It also discusses s

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Xu, Honghui, Yingshu Li, Olusesi Balogun, Shaoen Wu, Yue Wang, and Zhipeng Cai. "Security Risks Concerns of Generative AI in the IoT." IEEE Internet of Things Magazine 7, no. 3 (2024): 62–67. http://dx.doi.org/10.1109/iotm.001.2400004.

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Barrett, Clark, Brad Boyd, Elie Bursztein, et al. "Identifying and Mitigating the Security Risks of Generative AI." Foundations and Trends® in Privacy and Security 6, no. 1 (2023): 1–52. http://dx.doi.org/10.1561/3300000041.

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21

Noviandy, Teuku Rizky, Aga Maulana, Ghazi Mauer Idroes, et al. "Embrace, Don’t Avoid: Reimagining Higher Education with Generative Artificial Intelligence." Journal of Educational Management and Learning 2, no. 2 (2024): 81–90. https://doi.org/10.60084/jeml.v2i2.233.

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This paper explores the potential of generative artificial intelligence (AI) to transform higher education. Generative AI is a technology that can create new content, like text, images, and code, by learning patterns from existing data. As generative AI tools become more popular, there is growing interest in how AI can improve teaching, learning, and research. Higher education faces many challenges, such as meeting diverse learning needs and preparing students for fast-changing careers. Generative AI offers solutions by personalizing learning experiences, making education more engaging, and su

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Montalvo, Fernando, Kathren Pavlov, Phuoc Thai, Bijita Devkota, and Brandon Takahashi. "Machine Learning and Human Expertise in the Balance: Generative AI in Healthcare Settings." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 13, no. 1 (2024): 211–15. http://dx.doi.org/10.1177/2327857924131010.

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Generative artificial intelligence chatbots are undergoing a rapid surge in popularity and use adoption across various industries. Healthcare is one of the many industries undergoing shifts in knowledge acquisition and decision-making as medical professionals learn to interact with the technology and implement it in various aspects of their everyday practice. As a result, healthcare professionals and institutions need to understand the risks involved when generative AI is introduced into information-seeking and decision-making processes, as well as how individual differences among users shift

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Chen, Yan, and Pouyan Esmaeilzadeh. "Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges." Journal of Medical Internet Research 26 (March 8, 2024): e53008. http://dx.doi.org/10.2196/53008.

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As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding the potential uses of generative AI in health care becomes increasingly important. Generative AI, including models such as generative adversarial networks and large language models, shows promise in transforming medical diagnostics, research, treatment planning, and patient care. However, these data-intensive systems pose new threats to protected health information. This Viewpoint paper aims to explore various categories of generative AI in health care, including medical dia

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S, Renjith. "Enhancing Learning Outcomes with Generative AI: A Study on Attention Span of College Students." Commerce & Business Researcher 15, no. 2 (2024): 59–68. http://dx.doi.org/10.59640/cbr.v15i2.59-68.

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This study investigates the transformative potential of Generative Artificial Intelligence (AI) in enhancing learning experiences and addressing the issue of low attention span among college students. The research focuses on four key components of AI-generated pedagogy: reducing learning challenges, enhancing learning experiences, ethical considerations, and balancing benefits and risks. Utilizing a quantitative survey-based approach, data were collected from 57 college students in the Kottayam District. The findings reveal that students perceive Generative AI as having a positive impact on le

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Rauh, Maribeth, Nahema Marchal, Arianna Manzini, et al. "Gaps in the Safety Evaluation of Generative AI." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 1200–1217. http://dx.doi.org/10.1609/aies.v7i1.31717.

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Generative AI systems produce a range of ethical and social risks. Evaluation of these risks is a critical step on the path to ensuring the safety of these systems. However, evaluation requires the availability of validated and established measurement approaches and tools. In this paper, we provide an empirical review of the methods and tools that are available for evaluating known safety of generative AI systems to date. To this end, we review more than 200 safety-related evaluations that have been applied to generative AI systems. We categorise each evaluation along multiple axes to create a

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Blease, Charlotte, and John Torous. "ChatGPT and mental healthcare: balancing benefits with risks of harms." BMJ Mental Health 26, no. 1 (2023): e300884. http://dx.doi.org/10.1136/bmjment-2023-300884.

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Against the global need for increased access to mental services, health organisations are looking to technological advances to improve the delivery of care and lower costs. Since November 2022, with the public launch of OpenAI’s ChatGPT, the field of generative artificial intelligence (AI) has received expanding attention. Although generative AI itself is not new, technical advances and the increased accessibility of large language models (LLMs) (eg, OpenAI’s GPT-4 and Google’s Bard) suggest use of these tools could be clinically significant. LLMs are an application of generative AI technology

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Litan, Avivah. "Mitigate Sensitive Data Risks With ChatGPT." ITNOW 65, no. 4 (2023): 30. http://dx.doi.org/10.1093/itnow/bwad120.

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Abstract As the use of generative AI tools becomes commonplace, Avivah Litan, distinguished VP Analyst at Gartner, looks at the key steps that organisations can take to help protect their sensitive data.

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Vujović, Dušan. "Generative AI: Riding the new general purpose technology storm." Ekonomika preduzeca 72, no. 1-2 (2024): 125–36. http://dx.doi.org/10.5937/ekopre2402125v.

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Generative AI promises to revolutionize many industries (entertainment, marketing, healthcare, finance, and research) by empowering machines to create new data content inspired by existing data. It experienced exponential growth in recent years. In 2023 breakout year Gen AI impact reached 2.6-4.4 trillion USD (2.5-4.2% of global GDP). The development of modern LLM-based models has been facilitated by improvements in computing power, data availability, and algorithms. These models have diverse applications in text, visual, audio, and code generation across various domains. Leading companies are

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Neumann, Peter G. "Risks to the Public." ACM SIGSOFT Software Engineering Notes 48, no. 3 (2023): 4–7. http://dx.doi.org/10.1145/3599975.3599976.

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ChatBots and Generative AI more generally have really opened up a deluge of RISKS items in the past few months, along with an increased awareness of risks relating to arti - cial intelligence. This issue includes just the tip of the iceberg with one-liners on that subject, where it is almost impossible to summarize all the risks. PGN

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Yu, Youchun. "Research on the Competition Law Risks and Regulation Brought by Generative Artificial Intelligence." Highlights in Business, Economics and Management 9 (June 13, 2023): 454–61. http://dx.doi.org/10.54097/hbem.v9i.9093.

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The emergence of chatGPT signifies that Artificial Intelligence (AI) technologies for generating content will have a significant impact on people’s work and lifestyle. Nowadays, R&D of generative AI such as chatGPT has become the core domain in the latest wave of technological competition among nations, yet we must be aware that it may result in the disclosure of trade secrets, abuse of dominant market position and other market competition and monopoly risks. In order to regulate the advancement of generative artificial intelligence, it is imperative to refine the existing legislative fram

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Chauhan, Arun. "Generative artificial intelligence and misinformation warfare." International Journal of Multidisciplinary Research and Growth Evaluation 5, no. 4 (2024): 997–1002. http://dx.doi.org/10.54660/.ijmrge.2024.5.4.997-1002.

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This research paper explores the intersection of generative AI and misinformation warfare, focusing on the sophisticated capabilities of AI technologies like Generative Adversarial Networks (GANs) and their potential misuse in creating deceptive content. Utilizing a mixed-methods approach, the study combines an extensive literature review with empirical data from a survey of 125 respondents, revealing high levels of awareness and concern about AI-generated misinformation. The findings indicate that AI-generated misinformation significantly impacts public opinion and societal trust, highlightin

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Shumakova, N. I., J. J. Lloyd, and E. V. Titova. "Towards Legal Regulations of Generative AI in the Creative Industry." Journal of Digital Technologies and Law 1, no. 4 (2023): 880–908. http://dx.doi.org/10.21202/jdtl.2023.38.

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Objective: this article aims to answer the following questions: 1. Can generative artificial intelligence be a subject of copyright law? 2. What risks the unregulated use of generative artificial intelligence systems can cause? 3. What legal gaps should be filled in to minimize such risks?Methods: comparative legal analysis, sociological method, concrete sociological method, quantitative data analysis, qualitative data analysis, statistical analysis, case study, induction, deduction.Results: the authors identified several risks of the unregulated usage of generative artificial intelligence in

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Lyubenko, R. Ya, and O. H. Puhachova. "RISKS OF MAINSTREAM GENERATIVE AI FOR SOCIAL NETWORKING SITES’ RESEARCH." СОЦІАЛЬНІ ТЕХНОЛОГІЇ: АКТУАЛЬНІ ПРОБЛЕМИ ТЕОРІЇ ТА ПРАКТИКИ, no. 100 (2023): 64–74. http://dx.doi.org/10.32782/2707-9147.2023.100.5.

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周, 啸. "Research on Copyright Infringement Risks in Generative AI Data Use." Open Journal of Legal Science 12, no. 05 (2024): 3261–67. http://dx.doi.org/10.12677/ojls.2024.125463.

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Powell, Wardell, and Steven Courchesne. "Opportunities and risks involved in using ChatGPT to create first grade science lesson plans." PLOS ONE 19, no. 6 (2024): e0305337. http://dx.doi.org/10.1371/journal.pone.0305337.

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Generative AI can potentially support teachers in lesson planning by making the process of generating an outline more efficient. This qualitative study employed an exploratory case study design to examine a specific lesson design activity involving a series of prompts and responses from ChatGPT. The desired science lesson on heredity was aimed at first grade students. We analyzed the process’s efficiency, finding that within 30 minutes we could generate and substantially refine a lesson plan that accurately aligned with the desired curriculum framework and the 5E model of instruction. However,

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Baha, Benson Yusuf, and Omachi Okolo. "Navigating the Ethical Dilemma of Generative AI in Higher Educational Institutions in Nigeria using the TOE Framework." European Journal of Computer Science and Information Technology, 12, no. 8 (2024): 18–40. http://dx.doi.org/10.37745/ejcsit.2013/vol12n81840.

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Generative AI tools stand at the threshold of innovation and the erosion of the long-standing values of creativity, critical thinking, authorship, and research in higher education. This research crafted a novel framework from the technology, organization, and environment (TOE) framework to guide higher educational institutions in Nigeria to navigate the ethical dilemma of generative AI. A questionnaire was used to collect data from twelve higher institutions among lecturers, students, and researchers across the six (6) geopolitical zones of Nigeria. The structural equation modeling was used to

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Su, Hongxia, Huifen Liu, Shan Sun, and Mian Xu. "Application of Generative Artificial Intelligence Tool in Tourism in China." International Journal of Global Economics and Management 4, no. 3 (2024): 122–30. http://dx.doi.org/10.62051/ijgem.v4n3.15.

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With the rapid progress of artificial intelligence and natural language processing technology, large-scale language models such as ChatGPT are increasingly widely used in the tourism industry, profoundly changing the traditional mode of tourism services. This paper discusses the application of Generative AI tools in tourism and its challenges and risks from both the supply side and the demand side. The research points out that travel agencies are faced with the loss of tourists, technical threshold and cost pressure, and tourists also have certain risks when using Generative AI tools for trave

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Chen, Su-Yen. "Generative AI, learning and new literacies." Journal of Educational Technology Development and Exchange 16, no. 2 (2023): 1–19. http://dx.doi.org/10.18785/jetde.1602.01.

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Launched in November 2022, OpenAI’s ChatGPT garnered over 100 million users within two months, sparking a surge in research and concern over potential risks of extensive AI experiments. The article, originating from a conference presentation by Tsinghua University and NTHU, Taiwan, provides a nuanced overview of Generative AI. It explores the classifications, applications, governance challenges, societal implications, and development trajectory of Generative AI, emphasizing its transformative role in employment and education. The piece highlights ChatGPT’s significant impact and the strategic

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Zainuddin, Nurkhamimi. "Does Artificial Intelligence Cause More Harm than Good in Schools?" International Journal of Language Education and Applied Linguistics 14, no. 1 (2024): 1–3. http://dx.doi.org/10.15282/ijleal.v14i1.10432.

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The integration of artificial intelligence (AI) in schools presents significant challenges and risks requiring responsible and ethical management. Despite warnings from tech leaders, major corporations push AI adoption in schools, leading to privacy violations, biased algorithms and curricular misinformation. Generative AI, though enhancing resources, risks disseminating false information. Biased AI models perpetuate inequalities, especially for marginalized groups. The financial burdens of AI implementation worsen budget constraints, and AI-driven surveillance raises privacy concerns. Governa

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Yang, Tianfang. "Legal Regulation of Generative Artificial Intelligence in China." Law & Digital Technologies 4, no. 1 (2024): 25. http://dx.doi.org/10.18254/s278229070031786-0.

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The rapid development of generative artificial intelligence (AI), exemplified by technologies like ChatGPT, has prompted significant regulatory responses in China. This paper explores the legal framework established by China&apos;s Interim Measures for the Management of Generative Artificial Intelligence Services, highlighting its regulatory mechanisms and compliance obligations for AI service providers. The measures aim to address various risks associated with generative AI, such as data security, content management, and user protection, by implementing a dual registration system for algo

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Dhagare, Mr Rahul Prabhakar. "Generative AI and Education: A Symbiotic Relationship." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 1042–45. http://dx.doi.org/10.22214/ijraset.2024.65279.

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Generative AI, with its capacity to create diverse content formats, holds immense potential to revolutionize the educational landscape. This research paper delves into the multifaceted ways in which generative AI can enhance teaching and learning, fostering a symbiotic relationship between technology and human ingenuity. Personalized Learning: Generative AI can analyse vast datasets of student performance and engagement to create adaptive learning paths, tailoring instruction to individual needs and preferences. This personalized approach ensures that each learner receives the optimal level of

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Machleidt, Petr, Jitka Mráčková, and Karel Mráček. "Perception of the risks inherent in new AI technologies." TATuP - Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis 33, no. 2 (2024): 42–48. http://dx.doi.org/10.14512/tatup.33.2.42.

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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 r

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Yan, Miao, and Longfei Wang. "Assessing the Copyright Infringement Risk of Generative AI Created Works." Lecture Notes in Education Psychology and Public Media 53, no. 1 (2024): 99–107. http://dx.doi.org/10.54254/2753-7048/53/20240010.

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The rapid evolution of the Internet and big data technology has ushered in significant advancements in data production and utilization. However, this progress has brought to the forefront the intricate issue of copyright protection. This is particularly evident with the widespread adoption of generative artificial intelligence (AI), where concerns regarding data source protection, data processing standards, and the boundaries of data application have garnered considerable attention from both academia and industry. Big data mining techniques coupled with generative AI algorithms offer robust su

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Yeshwanth Vasa. "ETHICAL IMPLICATIONS AND BIAS IN GENERATIVE AI." International Journal for Research Publication and Seminar 15, no. 3 (2024): 500–511. http://dx.doi.org/10.36676/jrps.v15.i3.1541.

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This has led to several opportunities, mainly because generative AI has grown quickly, mostly in the content and services sectors. Since then, there have been some improvements to face these challenges, but there are still some urgent ethical issues and biases. This paper aims to discuss the following concerns concerning ethical questions around generative AI: privacy issues, the threat of misinformation, and issues related to intellectual property ownership. Also, it further proves the existence of bias in the systems and advocates for the consideration of race, gender, and other factors in s

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Yeshwanth Vasa. "ETHICAL IMPLICATIONS AND BIAS IN GENERATIVE AI." International Journal for Research Publication and Seminar 14, no. 5 (2023): 500–511. http://dx.doi.org/10.36676/jrps.v14.i5.1541.

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Abstract (sommario):

This has led to several opportunities, mainly because generative AI has grown quickly, mostly in the content and services sectors. Since then, there have been some improvements to face these challenges, but there are still some urgent ethical issues and biases. This paper aims to discuss the following concerns concerning ethical questions around generative AI: privacy issues, the threat of misinformation, and issues related to intellectual property ownership. Also, it further proves the existence of bias in the systems and advocates for the consideration of race, gender, and other factors in s

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Bhattacharya, Biswajit. "Generative AI in Healthcare Industry: Implementations and Challenges." International Journal for Research in Applied Science and Engineering Technology 12, no. 8 (2024): 1–5. http://dx.doi.org/10.22214/ijraset.2024.63815.

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Abstract: Generative AI (artificial intelligence) refers to algorithms and models that can be prompted to generate various types of content. Generative AI has quickly become a major factor in several industries, including health care. It has the potential to transform the sector, but we must understand how to use this technology in order to capitalize on its potential while avoiding the risks that may come with it while applying it to patient care. These models have played a crucial role in analyzing diverse forms of data, including medical imaging (encompassing image reconstruction, image-to-

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Kalia, Suman. "Potential Impact of Generative Artificial Intelligence(AI) on the Financial Industry." International Journal on Cybernetics & Informatics 12, no. 6 (2023): 37–51. http://dx.doi.org/10.5121/ijci.2023.120604.

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Presently, generative AI has taken center stage in the news media, educational institutions, and the world at large. Machine learning has been a decades-old phenomenon, with little exposure to the average person until very recently. In the natural world, the oldest and best example of a “generative” model is the human being - one can close one’s eyes and imagine several plausible different endings to one’s favorite TV show. This paper focuses on the impact of generative and machine learning AI on the financial industry. Although generative AI is an amazing tool for a discriminant user, it also

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Lodge, Jason. "AI in the wild." Pacific Journal of Technology Enhanced Learning 6, no. 1 (2024): 1. http://dx.doi.org/10.24135/pjtel.v6i1.176.

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It has been well over a year since ChatGPT emerged and brought with it much commentary about challenges and opportunities for education. There has been considerable discussion about risks to academic integrity and the possibilities of generative AI for enhancing learning and teaching. As the dust settles, the hard work of determining how exactly generative AI will integrate into higher education begins. In this session, we will explore the current state of generative AI in student learning. While the integration of generative AI into formal coursework has been inconsistent, to say the least, m

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De Silva, Daswin, Shalinka Jayatilleke, Mona El-Ayoubi, Zafar Issadeen, Harsha Moraliyage, and Nishan Mills. "The Human-Centred Design of a Universal Module for Artificial Intelligence Literacy in Tertiary Education Institutions." Machine Learning and Knowledge Extraction 6, no. 2 (2024): 1114–25. http://dx.doi.org/10.3390/make6020051.

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Generative Artificial Intelligence (AI) is heralding a new era in AI for performing a spectrum of complex tasks that are indistinguishable from humans. Alongside language and text, Generative AI models have been built for all other modalities of digital data, image, video, audio, and code. The full extent of Generative AI and its opportunities, challenges, contributions, and risks are still being explored by academic researchers, industry practitioners, and government policymakers. While this deep understanding of Generative AI continues to evolve, the lack of fluency, literacy, and effective

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Howell, Michael D., Greg S. Corrado, and Karen B. DeSalvo. "Three Epochs of Artificial Intelligence in Health Care." JAMA 331, no. 3 (2024): 242. http://dx.doi.org/10.1001/jama.2023.25057.

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ImportanceInterest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.ObservationsWhile AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began wit

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