The DOI matches its Crossref record; 114 of the 176 references it cites were found in a registry or an index.
Regulating ChatGPT and other Large Generative AI Models
Open accessAbstract
Large generative AI models (LGAIMs), such as ChatGPT, GPT-4 or Stable Diffusion, are rapidly transforming the way we communicate, illustrate, and create. However, AI regulation, in the EU and beyond, has primarily focused on conventional AI models, not LGAIMs. This paper will situate these new generative models in the current debate on trustworthy AI regulation, and ask how the law can be tailored to their capabilities. After laying technical foundations, the legal part of the paper proceeds in four steps, covering (1) direct regulation, (2) data protection, (3) content moderation, and (4) policy proposals. It suggests a novel terminology to capture the AI value chain in LGAIM settings by differentiating between LGAIM developers, deployers, professional and non-professional users, as well as recipients of LGAIM output. We tailor regulatory duties to these different actors along the value chain and suggest strategies to ensure that LGAIMs are trustworthy and deployed for the benefit of society at large. Rules in the AI Act and other direct regulation must match the specificities of pre-trained models. The paper argues for three layers of obligations concerning LGAIMs (minimum standards for all LGAIMs; high-risk obligations for high-risk use cases; collaborations along the AI value chain). In general, regulation should focus on concrete high-risk applications, and not the pre-trained model itself, and should include (i) obligations regarding transparency and (ii) risk management. Non-discrimination provisions (iii) may, however, apply to LGAIM developers. Lastly, (iv) the core of the DSA's content moderation rules should be expanded to cover LGAIMs. This includes notice and action mechanisms, and trusted flaggers.
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“For example, improperly secured LLMs could enable bad actors to generate fake patient data or insurance claims [62].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [62]
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Cites
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“Transparent LGAIMs connected to (good-old) knowledge bases, such as AtMan [43], present a promising way forward in this direction.”
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“LGAIMs are usually trained with several billion, if not hundreds of billions, parameters [43, 44], requiring large amounts of training data and computing power [45].”
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“Although the emergence of these models in recent years constitutes a significant technical advance (for foundations, see [39-43]), they harness, to a reasonable extent, existing technologies in a vastly increased scale and scope.”
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Training language models to follow instructions with human feedback
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Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
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Training Language Models to Follow Instructions with Human Feedback
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Items 401–493 of 493
Memorizing Documents with Guidance in Large Language Models
Park, B., & Choi, J. (2024) · International Joint Conference on Artificial Intelligence · doi:10.48550/arxiv.2406.15996 · Cited by 1
“Clarifying the knowledge location in LLMs is crucial for safe AI to protect user contents and believe the generated contents [ Hacker et al. , 2023 ] .”
BackgroundRegulating Chatbot Output via Inter-Informational Competition
Zhang, J. (2024) · Social Science Research Network · doi:10.2139/ssrn.4746556 · Cited by 1
Consumer Perspectives for Purchase Intentions of Online Pharmacy Products Using Deep Learning
Chatterjee, J., Neogi, S. G., Dwivedi, R. K., & Vashisht, A. (2024) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito61523.2024.10522354 · Cited by 4
“It is built on a family of large language models and has been fine-tuned using RL and supervised learning techniques [41, 42].”
MethodYang, C., Liang, P., & Ma, Z. (2024) · Science of Computer Programming · doi:10.48550/arxiv.2401.03653 · Cited by 1
“This helps stakeholders to understand the importance of assumptions in projects and how assumptions impact each other and other types of software artifacts.”
BackgroundLee, A. H. S., Shankararaman, V., & Ouh, E. L. (2023) · BigData Congress [Services Society] · doi:10.1109/bigdata59044.2023.10386189 · Cited by 1
“Ethical Implications and impact on policies [23][24][25][26] Based on the above, it seems to hint that the role of ChatGPT in citizen service delivery has yet to be fully explored beyond being an enhanced chatbot or search engine.”
BackgroundArchiGuesser - AI Art Architecture Educational Game
Ploennigs, J., Berger, M., & Carnein, E. (2023) · arXiv.org · doi:10.48550/arxiv.2312.09334 · Cited by 1
“The new challenges of generative AI in education are widely discussed from pros and cons [1, 8, 10] to regulatory implications [2, 11].”
BackgroundGhandour, A., & Woodford, B. J. (2023) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit58888.2023.10453750 · Cited by 2
“However this definition is now somewhat outdated as it does not include generative AI systems [3] .”
BackgroundChatGPT® and Knowledge of Brazilian Dental Ethics and Legislation
da Nóbrega, J. B. M., Gusmão, C. L. V., Laureano, I. C. C., & Santiago, B. M. (2026) · Pesquisa Brasileira em Odontopediatria e Clínica Integrada · doi:10.1590/pboci.2026.010 · Cited by 1
Large language models: A review of their impact on AI, industry, and society
Thakur, U. P., Balani, H., Rahangdale, H., Rathore, P. S., Wasnik, M., & Jatale, P. (2025) · AIP conference proceedings · doi:10.1063/5.0234110
Governance in Motion: Co-evolution of Constitutions and AI models for Scalable Safety
Huang, C., Shen, Z., Ren, Y., Zheng, H., Zhang, J., Chai, M., Zhang, M., Dou, S., Mo, F., Shi, J., et al. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.869
“– Holmes Jr (2020) Each year, governments revise laws to address past failures and adapt to new societal challenges (Aftab and Savitt, 1999; Voigt and Von dem Buss-che; Hacker et al., 2023).”
Exploring Deceptive Domain Transfer Strategies: Mitigating the Differences among Deceptive Domains
Shahriar, S., Mukherjee, A., & Gnawali, O. D. (2023) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-092-2_115 · Cited by 2
Democratising AI via Purpose Limitation for ModelsPreprint
Mühlhoff, R., & Ruschemeier, H. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4599869 · Cited by 5
Humanities and AI: Ethical Education in Technology Careers
Camargo, L. S., Blay, E. A., & Schmidt, G. S. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-79032-4_6 · Cited by 1
Diagnostic accuracy and citation integrity of four large language models on otolaryngology vignettes
Pennington-FitzGerald, W., Warrier, A., Durant, S., Sharaf, I., Carlino, F., Pamula, S. D., Eloy, J. A., & Levi, J. (2026) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-026-10253-5
“This concern is mirrored in ongoing regulatory conversations; the FDA and other international bodies have emphasized the importance of transparency, explainability, and auditability in clinical decision support tools, particularly those driven by machine learning [39, 40 ].”
The European Regulatory Mirage: Epistemic Dependence and the Limits of AI Governance
García-Llorente, C., & Olmeda, I. (2026) · Journal of Common Market Studies · doi:10.1111/jcms.70181
Wang, Y., Li, P., He, P., & 洪秋華 (2026) · Journal of Economic Surveys · doi:10.1111/joes.70175
Villarino, J. B. Y., & Rana, D. S. (2026) · AI for Society · doi:10.3390/aisoc1020006
Arif, S., & Fernández-Crehuet, J. M. (2026) · Information and Computer Security · doi:10.1108/ics-03-2026-0154
van den Boom, F. (2026) · Communications · doi:10.1515/commun-2025-0110
“…the need to go further: to imagine contestation not merely as a safeguard but as a structural right, enabling citizens to resist, adapt, and reconfigure ADM systems that fail to uphold the principles of justice, solidarity, and dignity on which democratic welfare states rest (Hacker et al., 2023).”
Zhang, H., Wu, R., Hou, W., Lan, P., & Hu, S. (2026) · Data Science and Management · doi:10.1016/j.dsm.2026.100205
LABELING AI-GENERATED CONTENT: PRIVACY TRADE‑OFFS AND MARKET EFFECTS OF ARTICLE 50 EU AI ACT
Tsyapa, N. (2026) · Socio-economic relations in the digital society · doi:10.55643/ser.3.61.2026.677
Tuyen, T. T. T., Vu, T. T., & Luu, N. N. T. (2026) · International Journal of Learning, Teaching and Educational Research · doi:10.26803/ijlter.25.9.23
Meta-Optics-Based Diffractive Learning Machine for Angle-Multiplexed Multi-Task Processing
Luo, M.-C., Xiong, J.-M., Peng, J.-Y., Hu, J.-T., Chen, H.-W., Shu, C., & Huang, C.-R. (2026) · doi:10.48550/arxiv.2609.25792
“More details about the experimental setup can be found in Supplementary Note 7.”
InfluentialLarge language models in medical time series analysisPreprint
Han, Y., Beyan, C., Zhang, X., Liu, X., Liu, N., Sun, J., Hong, S., Ding, C., & Murino, V. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2609.22262
The evolution and prospects of regulating artificial intelligence crime in China
Lin, Y.-J. (2026) · International Journal of Law, Crime and Justice · doi:10.1016/j.ijlcj.2026.100876
Bentley, S. V., EVANS, D. B., & McGrath, M. J. (2026) · Heliyon · doi:10.1016/j.heliyon.2026.e45279
LAAF: A Layered Accountability Architecture Framework for LLM Applications
Chaturvedi, P., Ahmad, S., Nowroozi, E., Waqas, M., Loukas, G., Jolfaei, A., Cordeiro, L., & Dantas, P. V. (2026) · arXiv.org · doi:10.48550/arxiv.2608.27102
“…since accountability-enabling disclosures also enable copying and attack; automation with substantive oversight, since review erodes the efficiency justifying deployment; standardisation with contextual fit, since a common template underspecifies each sector; traceability with privacy,…”
Knowledge Sufficiency in the Era of Contemporary Digital Technologies
Hussinki, H. (2026) · European Conference on Knowledge Management · doi:10.34190/eckm.27.1.4975
“In this paradigm, human creativity, know-how and reasoning are impossible to replace with digital technologies that are primarily designed to predict, classify or optimize (e.g. Hacker et al, 2023).”
Legal Aspects of Automatic Equipment in Medical Laboratories
Aprianda, A. D., Subhan, M., & Lany, A. (2026) · Jurnal Iman dan Spiritualitas · doi:10.15575/jis.v6i3.56012
“The use of these two general legal codes demonstrates the general application of the Indonesian legal system, where general provisions can be applied to specific cases when there is no specific law (Hacker et al., 2023).”
Affective Computing in the Age of GenAI: Balancing Risks and Rewards
Latif, S., Shoukat, M., Pervez, F., Shamshad, F., Usama, M., Sandhu, M., & Schuller, B. W. (2026) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2026.3678879
“Regulators and developers need to recognise synthetic socio-emotional data as a potential privacy threat and implement appropriate safe-guards [76], [77].”
Khan, A., Hussain, S., & Khan, F. N. (2026) · Quality & Quantity · doi:10.1007/s11135-026-03030-3
Galindez, W. (2026) · International Journal of Computational Innovations Intelligent Systems and AI · doi:10.64439/cisai.v2i2.46
Assisting or resisting patriarchy? a critical discourse analysis of chatgpt’s responses on feminism
Biswas, A., Sulzer, M., & Hasan, M. U. (2026) · AI and Ethics · doi:10.1007/s43681-026-01220-1
“First, we conceptualized the textual outputs as moment-in-time manifestations of the structural conditions that have shaped the software, namely, that ChatGPT has been a loosely regulated and widely used commercial product [ 19 ].”
Ha, S., & Min, Y. (2026) · Convergence The International Journal of Research into New Media Technologies · doi:10.1177/13548565261468610
Right to be Forgotten in the Deepfake Era
Michałkiewicz-Kądziela, E., & Staszewski, W. (2026) · Teka Komisji Prawniczej PAN Oddział w Lublinie · doi:10.32084/tkp.10696
“Waiting for a personal data breach weakens individual protection, as individuals can only react when the damage occurs ex post [Hacker, Engel, and Mauer 2023, 16] The proposed change in approach aims to develop preventive mechanisms that operate not at the moment of the event, but before the…”
Hussinki, H., Mikalef, P., & Ritala, P. (2026) · Knowledge Management Research & Practice · doi:10.1080/14778238.2026.2689386 · Cited by 5
"AI Watermarking": Bridging Policy Discourse and Technical CapabilitiesPreprint
Fábrega, A., Bhattacharya, A., Christ, M., & Park, S. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2606.28331
“Hacker et al. [34] offer a broad critique of existing GenAI regulation and some suggestions.”
Trace Data as a Privacy-Preserving Indicator of Compliance Behavior in External Generative AI Use
Kim, D., Valacich, J., Jenkins, J., & Wilson, D. (2026) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro70003.2026.11591671
“In practice, content-centric controls often require substantial operational tuning and can introduce user friction, particularly when policies over-redact or block inputs that users view as necessary for high-quality GenAI outputs [13].”
Geiß, S. (2026) · Communication and Change · doi:10.1007/s44382-026-00025-4
LEGAL REGULATION AND RISKS OF USING GENERATIVE ARTIFICIAL INTELLIGENCE
Kirillova, E., Blinkov, O., Korotkikh, O., Moroz, N., & Masterskikh, S. (2026) · Veredas do Direito · doi:10.18623/rvd.v23.6343
Generative AI jailbreaks as emerging cyberthreats: Ethical, legal, and societal implications
Teichmann, F. M. J. (2026) · International Cybersecurity Law Review · doi:10.1365/s43439-026-00171-x · Cited by 2
“However, if someone is, for example, generating dozens of violent manifestos with an AI, the provider might justifiably intervene or report this as inappropriate [18].”
A conversational tool for AI-driven feedback for UML modeling
Ardimento, P., Bernardi, M. L., Cimitile, M., & Scalera, M. (2026) · Software & Systems Modeling · doi:10.1007/s10270-026-01373-x
دراسة نقدية للائحة التنفيذية لقانون حماية البيانات الشخصية المصري: مشروعية التنظيم وكفاءته الوظيفية
Abou-elhasan, A. (2026) · Journal of Law and Emerging Technologies · doi:10.54873/jolets.v6i1.253
Bachmann, P. A., Boehmer, N., Klausner, L. D., & Lackner, M. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3806808
“In this paper, we therefore adopt the premise that some form of public oversight and regulation is appropriate and, indeed, necessary, while pointing to recent literature [12, 41, 42, 64] for a more comprehensive debate on the merits and limits of regulation of AI.”
Data protection in the AI era: benchmarking EU GDPR and AIA, to reform Saudi Data Protection law
Sarabdeen, J. (2026) · Cogent Social Sciences · doi:10.1080/23311886.2026.2652554
Trust-Aware Routing for Distributed Generative AI Inference at the Edge
Nguyen, C. L. T., & Elmroth, E. (2026) · International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT) · doi:10.1109/dcoss-iot69657.2026.00036
“…such centralized data centers exposes GenAI services to network disruptions, service outages, and increased privacy and data-governance complexity [10], particularly regarding sovereign data requirements, cross-jurisdictional handling [11], [12], and the risks inherent in large-scale data…”
Creative Agency and Ethical Boundaries: A Documentary Analysis of AI in the Creative Industries
Zengo, L. (2026) · Interdisciplinary Academic and Research Journal · doi:10.60027/iarj.2026.e290412
“Scholars such as Runco (2023) and Edwards (2023) examine whether AI possesses a form of autonomy sufficient to generate creative outputs independently of human input, and what implications this might hold for industries that have historically relied on human artistic intuition.”
Self-Service or Not? How to Guide Practitioners in Classifying AI Systems Under the EU AI Act
Schnitzer, R., Hoeving, M., & Zillner, S. (2026) · arXiv.org · doi:10.48550/arxiv.2603.00065
“Schuett [21] examines Article 9 on Risk Management, whereas Hacker et al. [9] analyze the regulatory challenges posed by technological advances, such as Generative AI and foundation models.”
Ethics and regulation of generative AI in medical device development
Phillips, S., Mccoll, D., & Duncan, A. (2026) · AI and Ethics · doi:10.1007/s43681-025-00947-7
“These roles are similar to some identified as part of an ’AI value chain’ or supply chain, defined as ‘developers, deployers, professional and non-professional users, as well as recipients of LGAIM (large generative AI model) out-put’ [31].”
The Use of Artificial Intelligence in Advocacy
Illýová, Z. M., & Hron, I. (2025) · Comenius : Bratislava legal forum · doi:10.46282/bpf.2025.11
Log in, lie down: ethics and the digital turn in psychotherapy
Branford, J., & Conrad, J. A. (2025) · Philosophical Psychology · doi:10.1080/09515089.2025.2599894
IntuitIQ - AI Driven Calculator
Bhattacharjee, H., Saha, D., Roy, A., Bal, P., Sarkar, K., Banerjee, S., & Sharma, K. (2025) · 2025 Modern Electronics Devices and Intelligent Communication Systems (MEDCOM) · doi:10.1109/medcom67532.2025.11405395
Jiang, Q., & Zuo, C. (2025) · Asian Review of Political Economy · doi:10.1007/s44216-025-00068-2
“The black box problem of algorithms, the continuous collection of new data in AI application, and the emergence of new functions, purposes, and algorithms from AI baseline models, make precise risk notification difficulty, if not impossible (Hacker et al. 2023).”
Test Case Generation for Context-Aware Large Language Models Based on Deep Semantic Understanding
Gao, C., Yin, J., Li, J.-L., Ge, S., & Kong, X. (2025) · 2025 4th International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR) · doi:10.1109/aihcir67580.2025.11404785
“Generative AI aims to replicate human-like output in multiple fields such as multimedia production, computer vision, and natural language processing (NLP) [5].”
Pagare, M., Bheesetti, D. S. K., Essien-Aleksi, I., & Alam, M. A. U. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf142
“15 LLMs inherit data biases, 18 struggle with reasoning and rare words, 19 and risk spreading disinformation.”
Li, J., Wang, W., Mao, H., Ni, J., & Xiong, Z. (2025) · International Conference on Human-Engaged Computing (ICHEC) · doi:10.1145/3786995.3787064
The Ethical Landscape of Generative AI: A Multi-Level Analysis of ChatGPT
Wang, Y. (2025) · Insights in computer, signals and systems. · doi:10.70088/y0gact38
“They are accurately classified as emerging technologies due to their rapid development cycle and the significant uncertainty surrounding their long-term applications and consequences [7].”
From Optimization to Application: AI Agent Systems and Ontologies for Legal Knowledge Extraction
Garrido, A., Herrmann, H. N., Peiro, A., & Mena, E. (2025) · International Conference on Computer Science and Information Technologies · doi:10.1145/3783862.3783898
“These include the creation of deceptive content like deepfakes [10], hallucinations or inaccurate outputs [11], and unclear intellectual property rights in both content generation and model training [12, 13].”
AIGC: From Statistical Methods to Cognitive Applications
Zhou, H., & Zeng, B. (2025) · International Conference on Computer Science and Information Technologies · doi:10.1145/3783862.3783894
“The large-scale datasets used to support the training of large models may violate users’ privacy rights regarding personal information, namely the issue of training data infringement [3].”
Guo, Q., Zhang, Y., Yuan, K., He, C., Peng, Z., & Ma, X. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757474
Communication Bias in Large Language Models: A Regulatory Perspective
Kuenzler, A., & Schmid, S. (2025) · arXiv.org · doi:10.48550/arxiv.2509.21075
“Content moderation is often considered essential in addressing communication bias, but content moderation mechanisms also raise concerns around freedom of expression [17].”
Facing GAIa: A Tale of Three ChatGPToxicities
Galanos, V., & Xi, I. (2025) · Deleuze and Guattari Studies · doi:10.3366/dlgs.2025.0609
Bitcoin's Transition from Commodity to Regulated Currency: Implications and Challenges
Seke, M. M. (2025) · International Journal of Communication and Networking System · doi:10.20894/ijcnes.103.014.001.001
The Contribution of AI in Climate Modeling and Sustainable Decision-Making
Panda, S. P., Muppala, M., & Koneti, S. B. (2025) · International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences · doi:10.37082/ijirmps.v13.i3.232551
Position: EU AI Act's Research Exemptions Can Break the Publication Norms of Major AI Conferences
Wernick, A., & Meding, K. (2025) · doi:10.48550/arxiv.2506.03218
“…exceptions are usually discussed in connection with another topic, such as copyright or data protection (Quintais, 2024; Quezada-Tavarez et al., 2022; De Vries, 2021) or their relevance for a specific application area or type of AI (Bublitz et al., 2024; Mann et al., 2024; Hacker et al., 2023).”
Multifactor SM9 blind signature large models data privacy preservation approach
Zhang, J.-Z., Li, S.-W., & Chen, M. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-01989-8
“Consequently, some European scholars also propose that the EU should transition its proposed comprehensive AI regulation to a system of specific regulatory responsibilities and content governance [7].”
GEN AI in Identity and Access Management: Transforming Security Solutions
Wairagade, A., Sonani, R., & Govindarajan, V. (2025) · 2025 3rd International Conference on Business Analytics for Technology and Security (ICBATS) · doi:10.1109/icbats66542.2025.11258464
“The ability to incorporate risk management protocols and transparency in its framework also helps in meeting the regulations of the European Union (EU) AI Act [32].”
BEYOND THE CODE: A REVIEW OF SOCIETAL CHALLENGES AND OPPORTUNITIES IN CONVERSATIONAL AI
Chalke, A., Cheng, B., Lee, T., Tenk, M. T. T., & Yeong, H. (2025) · Quantum Journal of Social Sciences and Humanities · doi:10.55197/qjssh.v6i2.639
Tapales, E. (2025) · Psychology and Education: A Multidisciplinary Journal · doi:10.70838/pemj.350303
Zhao, M., Mao, H., Zhu, E., Chen, S., Shi, E., González, C., Li, Y., Wang, J., Nganga, A., Kong, E., et al. (2025) · 2025 8th International Conference on Software and System Engineering (ICoSSE) · doi:10.1109/icosse65712.2025.00025
“In the final stage, Generation, the retrieved data is used to create a polished and coherent Policy Brief [9].”
GPT Adoption and the Impact of Disclosure PoliciesPreprint
Yang, C., Amariles, D. R., Allen, L., & Troussel, A. (2025) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2504.01566 · Cited by 4
“…2023), the conditions that give rise to specific legal risks and strategies for managing these risks (Charfeddine et al., 2024; Gupta et al., 2023; Hacker et al., 2023), and the types of monitoring systems and incentives that can be implemented to promote the responsible use of generative AI in…”
The Legality of AI-Generated Art: Copyright Ownership and Current Developments
Deckker, D., & Sumanasekara, S. (2025) · Lahore Journal of Business · doi:10.35536/ljb.2025.v13.i1.a4
“From this perspective, attributing authorship to AI raises ethical issues because machines cannot meet the moral responsibilities of rights-holders, such as respecting moral rights or being accountable (Hacker et al., 2023).”
Viertel, G. P., & Tabarés, R. (2025) · Teknokultura Revista de Cultura Digital y Movimientos Sociales · doi:10.5209/tekn.97799 · Cited by 1
Singh, G., Meenakshi, Sharma, P., & Bhardwaj, A. (2025) · 2025 International Conference on Pervasive Computational Technologies (ICPCT) · doi:10.1109/icpct64145.2025.10940335
“When fresh data is added to a generative AI system, it is added to the system's data repository and made accessible to other users in the public domain [13, 18].”
الجوانب القانونية للذكاء الاصطناعي التوليدي (تقنية شات جي بي تي Chat GPT كأنموذج)
محمد, أ. د. ع. ط. ب. (2025) · Journal of Law and Emerging Technologies · doi:10.54873/jolets.v4i1.205
Li, Y., & Xu, C. (2025) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2025-1019
Daşçı, E., & Uludağ, Z. (2024) · Trakya Üniversitesi sosyal bilimler dergisi/Trakya Üniversitesi Sosyal Bilimler dergisi · doi:10.26468/trakyasobed.1478054
TRAJETÓRIA DO CONSTITUCIONALISMO NO BRASIL
Barroso, L. R., & Mello, P. P. C. (2024) · REI - REVISTA ESTUDOS INSTITUCIONAIS · doi:10.21783/rei.v10i4.868
The Intersection of Generative AI and Wearable Health Devices: A Privacy-Focused Analysis
Thaker, H., & Thaker, B. (2024) · International Conference on Data, Computation and Communication (ICDCC) · doi:10.1109/icdcc62744.2024.10961527 · Cited by 1
“The regulation of these LLMs in medicine and healthcare without damaging their promising progress is a timely and critical challenge to ensure safety, maintain ethical standards, pre-empt unfairness and bias, and protect patient privacy [3].”
Trindade, A. A. (2024) · Anais da XIII Escola Regional de Informática de Mato Grosso (ERI-MT 2024) · doi:10.5753/eri-mt.2024.245867
de Carvalho, A. D., Bonidia, R., Kong, J., Dauhajre, M., Struchiner, C., Goedert, G., Stadler, P. F., Walter, M. E., Sanches, D., Day, T., et al. (2024) · arXiv.org · doi:10.48550/arxiv.2409.14181
“Law-making bodies in many countries and regions are passing new regulations regarding the development and use of AI to reduce or avoid risks associated with this technology [9, 10].”
ChatGPT in education: Its value, accuracy, and regulation
Pacala, F. A. (2024) · HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - SOCIAL SCIENCES · doi:10.46223/hcmcoujs.soci.en.15.2.3159.2025
“According to Hacker et al. (2023), these regulations might impose limitations on the gathering, storing, and using data produced by ChatGPT interactions.”
Lantyer, V. (2024) · Revista de Direito, Inovação, Propriedade Intelectual e Concorrência · doi:10.26668/indexlawjournals/2526-0014/2024.v10i1.10352
High Order Reasoning for Time Critical Recommendation in Evidence-based Medicine
Yu, M.-J., & Li, X. (2024) · arXiv.org · doi:10.48550/arxiv.2405.03010
“AI and NLP-powered chatbots can analyze extensive data and interpret complex medical scenarios, becoming valuable assistants in clinicians’ decision-making processes [3].”
BackgroundMissing Value Chain in Generative AI Governance China as an example
Pi, Y. (2024) · arXiv.org · doi:10.48550/arxiv.2401.02799
“This could potentially stifle competition and hinder innovation [10].”
BackgroundHartanto, R., Sari, D. P., & Sutrisno, B. (2023) · Journal of Technology Innovation and Society · doi:10.63646/jtis.2023.010103
“…new questions about how these frameworks allocate responsibility when a clinician relies on a model recommendation that subsequently proves incorrect, questions analyzed by Hacker, Engel, and Mauer (2023) and by Wachter, Mittelstadt, and Russell (2017) from a counterfactual-recourse perspective.”
InfluentialAziz, A. A., Nor, R. N. H., Jusoh, Y. Y., Rahman, W. N. W. A., Aziz, K. A., Tajuddin, N. I. I., & Muhammad, R. (2026) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2026.0170407
“Though the concept of information quality has long been in the focus of the information systems community, most of the existing literature was formulated in a setting that does not include GAI-generated content [5].”
Legal remedies for AI-generated reputational harm
Castro, L. H. (2026) · Law and Governance · doi:10.1017/cfl.2026.10062
Cooman, J. D. (2025) · Cambridge Forum on AI Law and Governance · doi:10.1017/cfl.2024.3
GENERATIVE ARTIFICIAL INTELLIGENCE: STUDENT LEARNING, ANXIETY, AND LEGALITY PERCEPTIONS
White, B. J., Dent, H. L., & Fulk, H. (2024) · Issues in Information Systems · doi:10.48009/4_iis_2024_105
Generative AI in Modern Education Society
Chakraborty, S. (2024) · arXiv.org · doi:10.48550/arxiv.2412.08666
“It is a frequently utilised tool in both personal and professional contexts due to its capacity to comprehend the context and give logical, contextually relevant solutions [3], [18], [24], [28], [29], [38], [39], [40], [42], [46], [55], [57], [58], [62].”
Tackling the Myriads of Collusion Scams on YouTube Comments of Cryptocurrency Videos
Shahriar, S., & Mukherjee, A. (2023) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-092-2_114
“Nevertheless, it is crucial to acknowledge that while chatGPT demonstrates responsible behavior by refraining from offering harmful or improper responses, it remains susceptible to manipulation by scammers (Hacker et al., 2023).”
BackgroundResearch on Tort Liability of Generative Artificial Intelligence
Gao, Z.-R. (2023) · Advances in Social Sciences · doi:10.12677/ass.2023.1212976
Metadata
- Authors
- Hacker, P., Engel, A., & Mauer, M.
- Affiliations
- European University Viadrina (DE)
- Heidelberg University (DE)
- Venue
- 2023 ACM Conference on Fairness Accountability and Transparency
- Publisher
- ACM
- Published
- 2023-06-12
- Pages
- 1112-1123
- OpenAlex
- W4380319827
- Cited by
- 466
- References
- 176
- Citations by year
- 2026: 59 · 2025: 151 · 2024: 191 · 2023: 65
- FWCI
- 1289.86
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by-nc-sa)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- Universität Hamburg
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
- Keywords
- ChatGPT, AI regulation, trustworthy AI, EU AI Act, AI value chain, high-risk AI systems, data protection, content moderation, Digital Services Act, transparency obligations, risk management, non-discrimination, pre-trained models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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