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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Sorted by citations
Items 301–400 of 493
From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint
Lambert, K., & Luccioni, S. (2026) · Conference on Fairness, Accountability and Transparency · doi:10.1145/3805689.3812401 · Cited by 4
“…at a crucial point in AI regulation when regulators are looking to researchers for experimental insights to inform and guide their policymaking [41]; by making results interpretable to non-technical stakeholders, researchers can better inform evidence-based regulation and contribute to…”
Invisible Safety Threat: Malicious Finetuning for LLM via SteganographyPreprint
Wan, G., Ma, X., Fang, G., & Wang, X. (2026) · International Conference on Learning Representations · doi:10.48550/arxiv.2603.08104 · Cited by 4
“Consequently, our method can evade a range of existing safety defenses, such as finetuning data filtering and inference-time monitoring (Hacker et al., 2023), thereby enabling an invisible form of safety threat.”
AIoT-Driven Health Behavioral Security: Vision and Challenges
Nakamura, Y. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3771551 · Cited by 4
“Moreover, the rise of generative AI intensifies these challenges, necessitating real-time, user-side interventions [2, 20, 42, 45, 62].”
A review on recent advancements of ChatGPT and datafication in healthcare applications
Jagatheesaperumal, S. K., Pandiyarajan, A., Prabadevi, B., Deepa, N., Barreto, A. G., & de Albuquerque, V. H. C. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110885 · Cited by 6
Safety of LLM-based AI chatbots for young consumers in purchase decisions
Tan, A. H. T., & Leo, K. C. (2025) · Young Consumers Insight and Ideas for Responsible Marketers · doi:10.1108/yc-10-2024-2277 · Cited by 8
Generative AI regulation can learn from social media regulation
Appel, R. (2024) · International Conference on Machine Learning · doi:10.48550/arxiv.2412.11335 · Cited by 4
“In fact, some scholars suggest to extend and adapt DSA rules for social media platforms to generative AI (Hacker et al., 2023).”
Ethical and Legal Considerations of Large Language Models: A Systematic Review of the Literature
Alkamli, S., Al-Yahya, M. M., & Alyahya, K. (2024) · 2024 2nd International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm63129.2024.10852451 · Cited by 4
“Existing regulations and guidelines often struggle to keep pace with the evolving capabilities of LLMs [11], leaving room for ambiguity and potential misuse.”
Generative Artificial Intelligence: Principles, Potentials and Challenges
Routray, S. K., Jha, M., Sharmila, K., Javali, A., Pappa, M., & Singh, M. (2024) · 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) · doi:10.1109/icssas64001.2024.10760992 · Cited by 4
“Both security and safety measures are needed to ensure the good and constructive use of generative AI [16].”
InfluentialThe state as a model for AI control and alignment
Elsner, M. (2024) · AI & Society · doi:10.1007/s00146-024-02063-2 · Cited by 5
“Some proposals have come from researchers (Hacker et al. 2023; Bengio et al. 2024).”
Regulatory solutions to alleviate the risks of generative AI models in qualitative research
Pillai, V. S., & Matus, K. (2024) · Journal of Asian Public Policy · doi:10.1080/17516234.2024.2399098 · Cited by 4
Identifying the Source of Generation for Large Language Models
Park, B., & Choi, J. (2024) · International Conferences on Pattern Recognition and Artificial Intelligence · doi:10.48550/arxiv.2407.12846 · Cited by 4
“Researchers discussed safety issues of LLMs worries such as false information, social bias, privacy infringement[9], and hallucinations[6,10,18].”
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Rey, B., Dufresne‐Camaro, C., & Irani, P. (2023) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3565066.3608700 · Cited by 4
Risk, Resilience, and Ethical Considerations in Artificial Intelligence
Buzzanell, P. (2023) · Emerging Media · doi:10.1177/27523543231188274 · Cited by 4
“…in homes with adults under extreme strains suffered violence during the pandemic; the elderly have been swayed more easily by scams and digital propaganda; ChatGPT and similar AI models have not been regulated suf fi ciently; Hacker et al., 2023; Lillywhite and Wolbring, 2023; Waymer, 2020).”
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ChatGPT-Crawler: Find out if ChatGPT really knows what it's talking aboutPreprint
Rangapur, A., & Wang, H. (2023) · arXiv.org · doi:10.48550/arxiv.2304.03325 · Cited by 4
Regulating AI with Purpose Limitation for Models
Mühlhoff, R., & Ruschemeier, H. (2024) · Journal of AI Law and Regulation · doi:10.21552/aire/2024/1/5 · Cited by 13
Tunes of tomorrow: Copyright and AI-generated music in the digital age
Surbhi, A., & Roy, D. (2024) · AIP conference proceedings · doi:10.1063/5.0234946 · Cited by 4
A Comparative Survey of Security Risks in AI Systems: From LLMs to AI Agents and Embodied Agents
Wu, B., Li, Q., Zhou, C., Wang, T., & Ji, S. (2026) · ACM Computing Surveys · doi:10.1145/3837083 · Cited by 2
Shahi, G. K., & Hummel, O. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2604.23430 · Cited by 2
“Instead, GAI focuses on creating new content by using the very data it was trained upon [16, 28].”
A pragmatic approach to regulating AI agentsPreprint
Hacker, P., & Holweg, M. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2604.22819 · Cited by 3
“This layered architecture reflects the recognition that model providers may see their products integrated into a range of downstream systems and must therefore enable, but cannot substitute for, system-level compliance (Hacker et al., 2023).”
Self-citationTrojanPraise: Jailbreak LLMs via Benign Fine-TuningPreprint
Xie, Z., Song, X.-R., & Luo, J. (2026) · arXiv.org · doi:10.48550/arxiv.2601.12460 · Cited by 3
“Fortunately, a de-fender can detect the harmful data provided by users by moderation models to mitigate these attacks (Hacker et al., 2023; Ji et al., 2023; Kolla et al., 2024; Kumar et al., 2024).”
Computational Compliance for AI Regulation: Blueprint for a New Research DomainPreprint
Marino, B., & Lane, N. D. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2601.04474 · Cited by 3
Zhou, S. (2025) · International Journal for the Semiotics of Law - Revue internationale de Sémiotique juridique · doi:10.1007/s11196-025-10388-4 · Cited by 2
“Regarding monitoring and reporting obligations, the EU’s AI Act encompasses provisions for regulatory oversight that address the transparency of AI models, risk management, non-discrimination clauses for large-scale generative AI models, and guidelines for content review [ 106 ].”
Musthafa, A. R., & Arundhati, G. B. (2025) · Review of European and Comparative Law · doi:10.31743/recl.18927 · Cited by 6
Not a Swiss Army Knife: Academics' Perceptions of Trade‐Offs around Generative AI Use
Razi, A., Bouzoubaa, L. A., Pessianzadeh, A., Seberger, J. S., & Rezapour, R. (2025) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.1277 · Cited by 2
“Hacker et al. (2023) argue that regulation should prioritize transparency and risk management and broaden content moderation guidelines to include governance in Gen AI models.”
Demystifying Chatbot Creation: A Comparative Case Study of Available Approaches
Muthyala, M. P., Lauer, C., Carradini, S., & Rajan, B. (2025) · IEEE Transactions on Professional Communication · doi:10.1109/tpc.2025.3587826 · Cited by 3
“The bot then goes through three meticulous checks that serve as guardrails to ensure the safety and integrity of the responses [27].”
Zucca, M. V., & Fiorinelli, G. (2025) · Technology and Regulation · doi:10.71265/23nqtq40 · Cited by 3
Content Moderation of Generative AI Prompts
Pardhi, P. R. (2025) · SN Computer Science · doi:10.1007/s42979-025-03864-y · Cited by 4
“In [23], the authors tackle the legal obstacles presented by the presence of large generative AI models (LGAIMs) such as ChatGPT [24] and offer suggestions on how the legal framework can be adjusted to account for their capabilities.”
Unfolding the Potential of Generative Artificial Intelligence
Bonnet, S., & Teuteberg, F. (2025) · International Journal of Knowledge Management · doi:10.4018/ijkm.368223 · Cited by 13
Regulating MultifunctionalityPreprint
Coglianese, C., & Crum, C. R. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2502.15715 · Cited by 2
“The term “foundation model” may also be colloquially used interchangeably with adjacent terms, such as “frontier,” “general-purpose,” “generative AI,” or “large generative AI models” [28].”
Dwivedi, D., & De', R. (2024) · Digit. Gov. Res. Pract. · doi:10.1145/3700142 · Cited by 3
“Existing AI regulatory frameworks, such as the European AI Act, are deemed inadequate for large generative AI models like ChatGPT [ 15 ], prompting calls for new laws focusing on data protection, consumer protection, cybersecurity, and intellectual property.”
AI Horizon Scanning, White Paper p3395, IEEE-SA. Part I: Areas of AttentionPreprint
Cortês, M., Liddle, A. R., Emmanouilidis, C., Kelly, A. E., Matusow, K., Ragunathan, R., Suess, J. M., Tambouratzis, G., Zalewski, J., & Bray, D. A. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2410.01808 · Cited by 4
“Based on transformers and pre-trained unsupervised deep-learning models, a class of AI technology, large generative AI models (LGAIM), has emerged with the capability to produce novel outputs of a sophistication not seen earlier [10].”
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Puchakayala, P. R. A. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.23.1.1999 · Cited by 7
“Generative AI empowers virtual assistants to evaluate a customer's financial history and present circumstances to deliver customised advice on savings, investments, or debt management [21].”
Ethical implications of Thai ChatGPT public discourses
Hongladarom, S., & Sakprasert, S. (2024) · Communication Research and Practice · doi:10.1080/22041451.2024.2346429 · Cited by 7
“However, possible regulation of large generative AI models such as ChatGPT are being seriously considered elsewhere (e.g. Hacker, Engel, & Mauer, 2023; Kandeel & Eldakak, 2024; Rao, 2023).”
Inteligencia artificial generativa: determinismo tecnológico o artefacto construido socialmente
Wilderbeek, F. L. L. D. C. (2024) · Palabra Clave · doi:10.5294/pacla.2024.27.1.9 · Cited by 3
Prolegomena to a Description Language for GenAI Tools in Cities
Degbelo, A. (2024) · Digital Government Research and Practice · doi:10.1145/3652952 · Cited by 3
“E, Synthesia, MusicLM) are a technology that is rapidly transforming the way we find information, communicate, illustrate, and create [11].”
Behind the Screen: Investigating ChatGPT's Dark Personality Traits and Conspiracy Beliefs
Weber, E., Rutinowski, J., & Pauly, M. (2024) · arXiv.org · doi:10.48550/arxiv.2402.04110 · Cited by 3
“[7] GPT-3.”
Generative AI and Wargaming: What is it Good For?
Hinton, P. (2023) · RUSI Journal · doi:10.1080/03071847.2023.2282863 · Cited by 3
Uprising of ChatGPT and Ethical Problems
Bang, J., & Park, G. (2023) · J-Institute · doi:10.22471/ai.2023.8.01 · Cited by 3
“Research on collaboration between humans and AI is being developed in various forms, including research on the Data Communication Model[34], and research on the impact of AI on human cognition[35].”
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Rane, N. L., Choudhary, S., & Rane, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4681735 · Cited by 2
Drivers of Trust in Generative AI-powered Voice Assistants: The Role of References
Widjaya, M. A., Bermúdez, J. P., Moradbakhti, L., & Calvo, R. A. (2023) · Electronic workshops in computing · doi:10.14236/ewic/bcshci2023.13 · Cited by 4
“Generative AI is a branch of artificial intelligence capable of producing new content such as text, images, and music (Hacker, Engel and Mauer, 2023).”
Atkinson, C. F. (2025) · doi:10.21203/rs.3.rs-5702366/v1 · Cited by 3
Near to Mid-term Risks and Opportunities of Open-Source Generative AI
Eiras, F., Petrov, A., Vidgen, B., de Witt, C. S., Pizzati, F., Elkins, K., Mukhopadhyay, S., Bibi, A., Csaba, B., Steibel, F., et al. (2024) · arXiv · doi:10.48550/arxiv.2404.17047 · Cited by 2
“Others (LAION.ai, 2023; Hacker et al., 2023; Tumad ´ ottir, 2023) highlight the need to differentiate open-source systems from a regulatory stand-point, to avoid compliance costs unsustainable for open source contributors (Parliament, 2023).”
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“Regarding the specific case of ChatGPT, researchers [19] [20] have outlined five key priorities for its role in research: focusing on human verification, developing rules for accountability, investing in truly open LLMs, embracing the benefits of AI, and widening the debate on LLMs. Open questions…”
BackgroundZhang, X., Zhou, W., Hu, W., Zhou, S., Hu, X., & Yang, L. (2025) · Technology in Society · doi:10.1016/j.techsoc.2025.102900 · Cited by 1
Generative Artificial Intelligence and the Ambiguity of Academic Integrity in Higher Education
Zdravkova, K. (2026) · Education sciences · doi:10.3390/educsci16071120 · Cited by 2
Yang, Y., Qi, J., & Ji, X. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2683905 · Cited by 2
Andrade, R. B. D. S., Rocha, G., Marques, G. D. S., & Canedo, E. (2026) · Brazilian Symposium on Information Systems · doi:10.5753/sbsi.2026.248735 · Cited by 2
Chakraborty, I., & Kumar, N. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124615 · Cited by 2
Regulating AI: Where U.S. State Policy and HCI (Mis)align
Migineishvili, N., Gao, A., Adjagbodjou, A. D., Thakur, D., Just, R., & Reinecke, K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791424
“Although we are interested in policy and responses to advances in large generative AI models (LGAIMs) [35] such as ChatGPT or Stable Diffusion, regulatory frameworks, including the EU AI Act, tend to address both conventional AI systems and LGAIMs [35].”
AmongOthers: A Design Speculation for Rethinking AI in Online Social Communities
Cho, H., Seo, J. A., Seo, W., & Yamashita, N. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790722 · Cited by 2
“While these contributions hold potential for providing emotional support [34, 140], they also raise new challenges, including copyright disputes [58], targeted harms toward marginalized groups [44, 59], and the production of hate speech [60].”
Çorum, F. C. (2026) · Hacettepe Hukuk Fakültesi Dergisi · doi:10.32957/hacettepehdf.1858048 · Cited by 2
Ball, C., Huang, K., Nie, X., Kong, E., & Dilinika, J. (2026) · Cyberpsychology, Behavior, and Social Networking · doi:10.1177/21522715261423789 · Cited by 2
Synergizing linguistic features and transformer networks for detecting AI-generated text
Yadagiri, A., Kumar, P., Poddar, Y., Pakray, P., & Chunka, C. (2026) · Knowledge and Information Systems · doi:10.1007/s10115-025-02637-6 · Cited by 2
“In the past, Hacker et al. [40] suggested using statistics to differentiate between writing produced by machines and humans.”
Navigating data protection in the European Union: achievements, challenges, and future directions
Monson, F. K. S., & Sackey, N. A. (2025) · International Cybersecurity Law Review · doi:10.1365/s43439-025-00157-1 · Cited by 4
“Studies show that the GDPR provoked heated discussions among experts who compliment the data protection norm for simplifying standards [50] yet others point out its technical difficulties and insuffi-cient tech transformation capabilities [28, 41].”
InfluentialA Contrastive Semantic Watermarking Framework for Large Language Models
Wang, J., Chang, X., Xiao, C., & Zhang, L. (2025) · Symmetry · doi:10.3390/sym17071124 · Cited by 2
Research on Security Risks and Supervision of Generative AI Large Models
Xu, C. (2025) · doi:10.1145/3744367.3744395 · Cited by 2
“…the application of the algorithm leads to adverse consequences, the controller usually takes advantage of the complexity and unexplainability of the technology to shirk responsibility, claiming that they have no intention for infringement or subjective motivation to do so intentionally [13].”
Gao, H., Zahedi, M., Jiang, W., Lin, H. Y., Davis, J. C., & Treude, C. (2026) · Empirical Software Engineering · doi:10.1007/s10664-026-10937-w · Cited by 6
“Additionally, production-grade chatbots such as ChatGPT and Gemini have raised concerns in potential failures, including policy compliance issues (Hacker et al., 2023), susceptibility to generating or endorsing violent content (Zhuo et al., 2023), and systemic bias (Fleisig et al., 2024).”
What Democracy Are We Talking About?
Karppinen, K., Moe, H., & Svensson, J. (2025) · Journal of Information Policy · doi:10.5325/jinfopoli.15.2025.0002
Examining the Threat Landscape of Generative AI: Attack Vectors and Mitigation Strategies for LLMs
Taylor, Z., Sharma, A., & Upadhyay, K. (2025) · Computing and Communication Workshop and Conference · doi:10.1109/ccwc62904.2025.10903828 · Cited by 2
“Generative AI represents a significant leap in artificial intelligence, evolving from systems that analyze data or follow preset rules to those that can create original content [1].”
ChatGPT Calls for Self Reflection: Student Perceptions of Evaluation Activities in Video
Hayashi, V. T., & Paiva, H. M. (2024) · IEEE International Conference on Teaching, Assessment, and Learning for Engineering · doi:10.1109/tale62452.2024.10834346 · Cited by 1
“In fact, ChatGPT brought a change so significant that not only its regulation [7, 8, 9], but extreme measures such as complete prohibition have been proposed and adopted at the institutional education level [10] and even by an entire country [11] (in this last case, temporarily).”
Deep Learning Approaches for Identifying AI-Generated Text via Feature Extraction
Yadagiri, A., Kalita, D., Ranjan, A., Bostan, A. K., Toppo, P., & Pakray, P. (2024) · IEEE Silchar Subsection Conference (SILCON) · doi:10.1109/silcon63976.2024.10910333
“A primary concern is their ability to generate false information or propagate misinformation [2].”
Muthyala, M. P., Lauer, C., & Carradını, S. (2024) · ACM International Conference on Design of Communication (SIGDOC) · doi:10.1145/3641237.3691661 · Cited by 1
“The bot then goes through three meticulous checks that serve as guardrails to ensure the safety and integrity of the responses [18].”
Alves, K., Santos, E., Silva, M., Chaves, A., Fernandes, J. A., Valença, G., & Brito, K. (2024) · International Conference on Theory and Practice of Electronic Governance · doi:10.1145/3680127.3680219 · Cited by 2
Chauhan, K. D. S. (2024) · International Review of Law, Computers & Technology · doi:10.1080/13600869.2024.2364989 · Cited by 2
“In other words, discriminative AI can tell you whether the animal in a picture is a cat; generative AI can actually draw you a picture of a cat (Hacker, Engel, and Mauer 2023, 3).”
Schmitt, V., Tesch, J., Garcia-Lopez, E., Polzehl, T., Burchardt, A., Neumann, K., Mohtaj, S., & Möller, S. (2024) · LEGAL · doi:10.63317/4zpprv95d9sz · Cited by 2
“Nonetheless, individuals determined to use ChatGPT and similar LGAIMs, such as Mixtral and Llama 2, to create deceptive or harmful content will discover methods to elicit such responses.”
BackgroundMethodResultInfluentialQuintais, J., Katzenbach, C., Schwemer, S. F., Dergacheva, D., Riis, T., Mezei, P., Harkai, I., & Magalhães, J. C. (2024) · IIC - International Review of Intellectual Property and Competition Law · doi:10.1007/s40319-023-01409-5 · Cited by 2
Unmasking the Source: Identifying Human Vs ChatGPT-Generated Text Through Machine Learning
Singh, P., Singh, A., Rathi, S., & Vasesi, S. (2023) · International Conference on Advanced Computing & Communication Technologies (ICACCTech) · doi:10.1109/icacctech61146.2023.00106 · Cited by 1
The opaque law of artificial intelligencePreprint
Calderonio, V. (2023) · arXiv.org · doi:10.48550/arxiv.2310.13192 · Cited by 2
“…more such as Luminous, Bard, Bing for text generation, Stable Diffusion, DALL·E 2 for images, Syntesia for video and MusicLM for audio; see P. HACKER et al. Regulating ChatGPT and other Large Generative AI Models , p. 12. technology today you can be convinced that this goal has been reached 17 .”
BackgroundHow ethics operationalization breaks down in practice: expert perspectives from healthcare AI
Minocher, R., Velarde, I., Bürger, V., Pluktaite, G., & Madai, V. I. (2026) · AI and Ethics · doi:10.1007/s43681-026-01381-z · Cited by 1
“Regula - tory frameworks continue to evolve rapidly, while techni - cal capabilities and application contexts change at a similar pace, making it difficult to establish stable conditions under which operationalization methods can be meaningfully compared or evaluated [ 35 ].”
Artificial intelligence in financial markets: a literature review and research agenda
Gong, S.-S., Han, S., Kou, Z.-Z., Zhang, Y., & Zhu, L. (2026) · China Accounting and Finance Review · doi:10.1108/cafr-08-2025-0189 · Cited by 1
Lundstrom-Imanov, G. O. Y. L.-F., & Abdullayeva, N. (2026) · arXiv.org · doi:10.48550/arxiv.2605.21002 · Cited by 1
“The second concerns automated compliance for AI systems, with Hacker, Engel, and Mauer [36] and Kop [37] as anchor references for the European context, joined more recently by Veale and Borgesius [38].”
Brodny, J., Tutak, M., & Kalbron, P. (2026) · Management Systems in Production Engineering · doi:10.2478/mspe-2026-0025 · Cited by 1
“In this paradigm, accidents at work are interpreted as the result of identifiable and separable causes, such as human error, technical failure, or improper work organization [8, 14].”
Monsalve-Alamá, A., Ortigosa-Blanch, A., & Martí-Sánchez, M. (2026) · ESIC Market · doi:10.7200/esicm.57.397 · Cited by 1
An LLM-Enabled Multimodal Agentic AI Framework for the Medical Internet of Things (MIoT)
Rahman, M. A., Jamil, S. U., Hossain, M. S., Khan, M. A., Zia, T. A., Paracha, M. A., Alrashoud, M. R., Chen, M., & Al-Hazzaa, S. A. F. (2026) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3805047 · Cited by 1
“When extended into multimodal agentic systems [10, 58], LLMs become central orchestrators that integrate heterogeneous inputs, plan complex workflows, invoke specialized tools, and coordinate multiple agents.”
Ogunsusi, K., Ayisi, R. K. K., Offei, A. N. A., Giami, K. L., & Ogunsusi, K. B. (2026) · American Journal of Applied Statistics and Economics · doi:10.54536/ajase.v5i1.6986 · Cited by 1
“…Kumari et al., 2025) - Bias detection and fairness in financial AI (Tekale, 2024; Saha et al., 2025) - Regulatory uncertainty and global compliance (Hacker et al., 2023; G’sell, 2024) - Human-in-command and risk literacy (Aldasoro et al., 2024; Chauhan et al., 2025) offer forward-looking values…”
The Regulation of Artificial Intelligence in the Legal Profession
Bhatti, N. (2026) · American Journal of Society and Law · doi:10.54536/ajsl.v5i1.7210 · Cited by 1
“…by an acute threat of confabulation and professional duty violation, is the development of generative AI - such as large language models that can be asked to draft pleadings and memoranda, along with correspondence to clients (Linna, 2025; Hacker et al., 2023; Larson, 2025; Ben-Daniel, 2025).”
Akıncı, M. F. (2026) · İstanbul Medeniyet Üniversitesi Hukuk Fakültesi Dergisi · doi:10.58733/imhfd.1879761 · Cited by 1
Communication Bias in Large Language Models: A Regulatory Perspective
Kuenzler, A., & Schmid, S. (2026) · Communications of the ACM · doi:10.1145/3769689 · Cited by 1
“17 But this process has technical limitations.”
Lang, J.-Q., Zhao, J.-H., Li, L.-J., & Zeng, D. (2026) · Electronics · doi:10.3390/electronics15040771 · Cited by 1
Lamchek, J. S., & Trieu, V. (2026) · Technology and Regulation · doi:10.71265/esg7cf11 · Cited by 1
Min, C.-H., Lee, D.-G., & Kwak, J. (2026) · Electronics · doi:10.3390/electronics15020307 · Cited by 1
Exploring organizational implementation of generative AI: navigating benefits and safety risks
Fox, G., & Solihin, Y. (2026) · Journal of Decision Systems · doi:10.1080/12460125.2026.2649940 · Cited by 1
Human in the loop chain of code prompting for deterministic tool development with generative AI
Atkinson, C. F. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00704-z · Cited by 5
da Rocha, U. N., Bonidia, R. P., Kong, J. D., Dauhajre, M., Struchiner, C. J., Goedert, G. T., Stadler, P. F., Sanches, D. S., Day, T., Castro, M. C., et al. (2025) · Microbial Biotechnology · doi:10.1111/1751-7915.70256 · Cited by 2
“Consequently, 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 (Anderljung et al. 2023; Hacker et al. 2023).”
Artificial Surrealism and Authors’ Rights
Dermawan, A. (2025) · Santander Art and Culture Law Review · doi:10.4467/2450050xsnr.25.009.21965 · Cited by 1
Emergent defenders: generative AI’s role in safeguarding IoT ecosystems
Rani, P., Sehrawat, H., Kaur, A., & Damaševičius, R. (2025) · Cluster Computing · doi:10.1007/s10586-025-05126-1 · Cited by 2
AI credibility and customer satisfaction: a neuroscience-based approach
Zubair, M., Iqbal, S., Kashif, M., Kosonogov, V., & Шестакова, А. Н. (2025) · Journal of Consumer Marketing · doi:10.1108/jcm-10-2024-7280 · Cited by 1
Sun, J., Zhao, R., Lin, L., Chi, Y., Leung, V. C. M., & Cai, W. (2025) · IEEE Systems Man and Cybernetics Magazine · doi:10.1109/msmc.2024.3482691
“This field faces challenges in regulating fine-tuning and feedback datasets to prevent privacy breaches and copy-right infringement.”
Mitigating Risks in Marketplace Semantic Search: A Dataset for Harmful and Sensitive Query Alignment
Spacek, F., Vančura, V., & Kordík, P. (2025) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3699682.3728329 · Cited by 1
FedGAI: Federated Style Learning with Cloud-Edge Collaboration for Generative AI in Fashion Design
Wu, M.-Z., Jiang, J.-N., Li, X.-L., Deng, H., & Wu, D. (2025) · arXiv.org · doi:10.48550/arxiv.2503.12389 · Cited by 1
“With the dramatic growth of generative AI models, data, and parameters [18], [30], the application of Federated Learning (FL), as a collaborative cloud-edge inference mechanism, not only facilitates cooperation among multiple clients but also offers significant advantages in enhancing resource…”
Abdelkarim, Y. A. (2025) · Journal of Research, Innovation and Technologies (JoRIT) · doi:10.57017/jorit.v4.1(7).03 · Cited by 1
“Furthermore, the intense employment of large generative AI models to manufacture and program AI machines enhances their online data generativity to produce online texts, images, etc. (Hacker et al. 2023, 1113).”
AI chatbots in Research: Yes or No? A Self-Reflective Exploration
Chia, C.-K., Rames, A., & Razak, A. Z. A. (2025) · Pertanika journal of science & technology · doi:10.47836/pjst.33.1.17 · Cited by 1
“Users may struggle to discern between original and erroneous content, potentially leading to its unwitting use in research (Hacker et al., 2023).”
Ethical AI Deployment: Balancing Innovation with Responsibility in Pre- Generative AI Systems
Vangala, V. R. (2024) · The Eastasouth Journal of Information System and Computer Science · doi:10.58812/esiscs.v2i02.710 · Cited by 1
Thompson, H. A. (2025) · Kyklos · doi:10.1111/kykl.70016 · Cited by 2
“…(Arbel and Hoffman 2024; Engel and McAdams 2024; Liu and Li 2024; Posner and Saran 2025; Choi 2025), how governments ought to regulate or govern AI (Hacker et al. 2023; Korinek 2024; Agrawal et al. 2025; O'Keefe et al. 2025; Bullock et al. 2024), how generative AI is likely to shape the practice…”
Sousa, S., Trügler, A., & Kern, R. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10547-x · Cited by 3
“In addition, BLOOM and GPT-2 are building blocks for generative AI, which also needs to follow trustworthy AI requirements to achieve beneficial societal uses [30].”
Moral Self-correction is Not An Innate Capability in Language Models
Liu, G., Qi, Z., Zhang, X., Cheng, L., & Johnson, K. (2025) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2025.findings-ijcnlp.39
“Our findings are in line with previous claims that LLMs are statisticians (Hacker et al., 2023; van Dijk et al., 2023), and LLMs rely on shallow heuristics for tasks requiring social intelligence (Aru et al., 2023; Shapira et al., 2024).”
Thorns and Algorithms: Navigating Generative AI Challenges Inspired by Giraffes and AcaciasPreprint
Hussain, W. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2407.11360 · Cited by 3
“In response, society is crafting new strategies to temper AI’s influence, creating mitigation strategies [5, 10], regulations [11,12], governance frameworks [13,14], and other benchmarks and safeguards [15] that reflect a deep-seated commitment to responsibly shaping technological advancements.”
BackgroundA Multifaceted Approach at Discerning Redditors Feelings Towards ChatGPT
Padarha, S., & Vijaylakshmi, S. (2024) · EAI Endorsed Transactions on Internet of Things · doi:10.4108/eetiot.6447 · Cited by 2
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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