Generative Ai in EU Law: Liability, Privacy, Intellectual Property, and Cybersecurity

Language Models are Few-Shot Learners

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Survey of Hallucination in Natural Language Generation

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Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

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On the Opportunities and Risks of Foundation Models

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Scaling Instruction-Finetuned Language Models

Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.11416 · Cited by 1,441 · see citing works

Extracting Training Data from Diffusion ModelsPreprint

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Toward Controlled Generation of TextPreprint

Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., & Xing, E. P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00955 · Cited by 802 · see citing works

Extracting Training Data from Large Language Models

Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T., Song, D., Erlingsson, U., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07805 · Cited by 727 · see citing works

Universal Adversarial Triggers for Attacking and Analyzing NLP

Wallace, E., Feng, S., Kandpal, N., Gardner, M., & Singh, S. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1221 · Cited by 642 · see citing works

Accountability in artificial intelligence: what it is and how it works

Novelli, C., Taddeo, M., & Floridi, L. (2024) · AI & SOCIETY · doi:10.1007/s00146-023-01635-y · Cited by 495 · see citing works

Ethical and social risks of harm from Language Models

Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2112.04359 · Cited by 477 · see citing works

Regulating ChatGPT and other Large Generative AI Models

Hacker, P., Engel, A., & Mauer, M. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594067 · Cited by 466 · see citing works

The law of everything. Broad concept of personal data and future of EU data protection law

Purtova, N. (2018) · Law Innovation and Technology · doi:10.1080/17579961.2018.1452176 · Cited by 352 · see citing works

Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models

Dahl, M., Magesh, V., Suzgun, M., & Ho, D. E. (2024) · Journal of Legal Analysis · doi:10.1093/jla/laae003 · Cited by 288 · see citing works

Does learning require memorization? a short tale about a long tail

Feldman, V. (2020) · Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing · doi:10.1145/3357713.3384290 · Cited by 287 · see citing works

Algorithms that remember: model inversion attacks and data protection law

Veale, M., Binns, R., & Edwards, L. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0083 · Cited by 278 · see citing works

Governing AI safety through independent audits

Falco, G., Shneiderman, B., Badger, J., Carrier, R., Dahbura, A., Danks, D., Eling, M., Goodloe, A., Gupta, J., Hart, C., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00370-7 · Cited by 250 · see citing works

Predictability and Surprise in Large Generative Models

Ganguli, D., Hernandez, D., Lovitt, L., Askell, A., Bai, Y., Chen, A., Conerly, T., Dassarma, N., Drain, D., Elhage, N., et al. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533229 · Cited by 207 · see citing works

Humans forget, machines remember: Artificial intelligence and the Right to Be Forgotten

Li, T., Villaronga, E. F., & Kieseberg, P. (2017) · Computer Law and Security Review · doi:10.1016/j.clsr.2017.08.007 · Cited by 173 · see citing works

Towards ethical and socio-legal governance in AI

Theodorou, A., & Dignum, V. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0136-y · Cited by 156 · see citing works

Generative language models and automated influence operations: Emerging threats and potential mitigations

Goldstein, J. A., Sastry, G., Musser, M., DiResta, R., Gentzel, M., & Sedova, K. (2023) · arXiv · doi:10.48550/arxiv.2301.04246 · Cited by 152 · see citing works

Mixout: Effective Regularization to Finetune Large-scale Pretrained Language ModelsPreprint

Lee, C., Cho, K., & Kang, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.11299 · Cited by 150 · see citing works

Disruptive and avoidable: GDPR challenges to secondary research uses of data

Péloquin, D., Dimaio, M., Bierer, B., & Barnes, M. (2020) · European Journal of Human Genetics · doi:10.1038/s41431-020-0596-x · Cited by 149 · see citing works

Copyright and Artificial Creation: Does EU Copyright Law Protect AI-Assisted Output?

Hugenholtz, P. B., & Quintais, J. P. (2021) · GRURRR. Gewerblicher Rechtsschutz und Urheberrecht, Rechtsprechungs-Report/GRUR-DVD/GRUR-CD/IIC/Gewerblicher Rechtsschutz und Urheberrecht/Gewerblicher Rechtsschutz und Urheberrecht. Internationaler Teil · doi:10.1007/s40319-021-01115-0 · Cited by 142 · see citing works

The European AI Liability Directives - Critique of a Half-Hearted Approach and Lessons for the FuturePreprint

Hacker, P. (2022) · Computer Law and Security Review · doi:10.48550/arxiv.2211.13960 · Cited by 141 · see citing works

Taking AI risks seriously: a new assessment model for the AI Act

Novelli, C., Casolari, F., Rotolo, A., Taddeo, M., & Floridi, L. (2024) · AI & SOCIETY · doi:10.1007/s00146-023-01723-z · Cited by 139 · see citing works

Inferring the demographics of search users

Bi, B., Shokouhi, M., Kosiński, M., & Graepel, T. (2013) · International World Wide Web Conference (WWW) · doi:10.1145/2488388.2488401 · Cited by 137 · see citing works

A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

Tonmoy, S. M. T. I., Zaman, S. M. M., Jain, V., Rani, A., Rawte, V., Chadha, A., & Das, A. (2024) · arXiv · doi:10.48550/arxiv.2401.01313 · Cited by 129 · see citing works

Artificial Intelligence and the Copyright DilemmaPreprint

Hristov, K. (2016) · SSRN Electronic Journal · Cited by 123 · see citing works

Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine LearningPreprint

Jo, E. S., & Gebru, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.10389 · Cited by 102 · see citing works

AI Risk Assessment: A Scenario-Based, Proportional Methodology for the AI Act

Novelli, C., Casolari, F., Rotolo, A., Taddeo, M., & Floridi, L. (2024) · Digital Society · doi:10.1007/s44206-024-00095-1 · Cited by 97 · see citing works

Improving Factuality and Reasoning in Language Models through Multiagent Debate

Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., & Mordatch, I. (2023) · arXiv · doi:10.48550/arxiv.2305.14325 · Cited by 96 · see citing works

Generating Fake Cyber Threat Intelligence Using Transformer-Based Models

Ranade, P., Piplai, A., Mittal, S., Joshi, A., & Finin, T. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9534192 · Cited by 95 · see citing works

An Overview of Privacy in Machine LearningPreprint

Cristofaro, E. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.08679 · Cited by 91 · see citing works

Do Androids Dream of Electric Copyright? Comparative Analysis of Originality in Artificial Intelligence Generated WorksPreprint

Guadamuz, A. (2018) · SSRN Electronic Journal · doi:10.17605/osf.io/v9nzb · Cited by 78 · see citing works

How Language Model Hallucinations Can Snowball

Zhang, M., Press, O., Merrill, W., Liu, A., & Smith, N. A. (2023) · arXiv · doi:10.48550/arxiv.2305.13534 · Cited by 77 · see citing works

Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering

Adlakha, V., BehnamGhader, P., Lu, X. H., Meade, N., & Reddy, S. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00667 · Cited by 74 · see citing works

Cognitive Mirage: A Review of Hallucinations in Large Language Models

Ye, H., Liu, T., Zhang, A., Hua, W., & Jia, W. (2023) · arXiv · doi:10.48550/arxiv.2309.06794 · Cited by 61 · see citing works

Understanding the legal provisions that allow processing and profiling of personal data—an analysis of GDPR provisions and principles

Gil González, E., & de Hert, P. (2019) · ERA Forum · doi:10.1007/s12027-018-0546-z · Cited by 51 · see citing works

Artificial Intelligence and Intellectual Property

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Right to be forgotten in the Era of large language models: implications, challenges, and solutions

Zhang, D., Finckenberg-Broman, P., Hoang, T., Pan, S., Xing, Z., Staples, M., & Xu, X. (2024) · AI and Ethics · doi:10.1007/s43681-024-00573-9 · Cited by 47 · see citing works

Intellectual Property Justification for Artificial Intelligence

Hilty, R. M., Hoffmann, J., & Scheuerer, S. (2021) · doi:10.1093/oso/9780198870944.003.0004 · Cited by 42 · see citing works

Vulnerability and Data Protection Law

Malgieri, G. (2023) · doi:10.1093/oso/9780192870339.001.0001 · Cited by 42 · see citing works

Identifiability and the applicability of data protection to big data

Oostveen, M. (2016) · International Data Privacy Law · doi:10.1093/idpl/ipw012 · Cited by 39 · see citing works

Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis

Xiao, Y., Liang, P. P., Bhatt, U., Neiswanger, W., Salakhutdinov, R., & Morency, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.538 · Cited by 34 · see citing works

Health Research, Consent and the GDPR Exemption.

Donnelly, M., & McDonagh, M. (2019) · European Journal of Health Law · doi:10.1163/15718093-12262427 · Cited by 34 · see citing works

Untitled work

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Patentability of AI-Generated Inventions: Is a Reform of the Patent System Needed?Preprint

Ramalho, A. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3168703 · Cited by 32 · see citing works

Text and data mining exceptions in the development of generative AI models: What the EU member states could learn from the Japanese “nonenjoyment” purposes?

Dermawan, A. (2023) · The Journal of World Intellectual Property · doi:10.1111/jwip.12285 · Cited by 31 · see citing works

European Copyright Licensing and Infringement Liability Under Art. 17 DSM-Directive Compared to Secondary Liability of Content Platforms in the U.S. – Can We Make the New European System a Global Opportunity Instead of a Local Challenge?Preprint

Leistner, M. (2020) · SSRN Electronic Journal · Cited by 28 · see citing works

Big Data and Analytics in the Age of the GDPR

Bonatti, P., & Kirrane, S. (2019) · 2019 IEEE International Congress on Big Data (BigDataCongress) · doi:10.1109/bigdatacongress.2019.00015 · Cited by 25 · see citing works

The European Commission’s Approach To Extra-Contractual Liability and AI – A First Analysis and Evaluation of the Two ProposalsPreprint

Dheu, O., Bruyne, J. D., & Ducuing, C. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4239792 · Cited by 24 · see citing works

AI Derivatives: the Application to the Derivative Work Right to Literary and Artistic Productions of AI MachinesPreprint

Gervais, D. J. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4022665 · Cited by 22 · see citing works

Machine Unlearning: Its Nature, Scope, and Importance for a “Delete Culture”

Floridi, L. (2023) · Philosophy & Technology · doi:10.1007/s13347-023-00644-5 · Cited by 20 · see citing works

It’s All in the Name: A Character-Based Approach to Infer Religion

Chaturvedi, R., & Chaturvedi, S. (2023) · Political Analysis · doi:10.1017/pan.2023.6 · Cited by 18 · see citing works

The EU’s cybersecurity framework: the interplay between the Cyber Resilience Act and the NIS 2 Directive

Eckhardt, P., & Kotovskaia, A. (2023) · International Cybersecurity Law Review · doi:10.1365/s43439-023-00084-z · Cited by 18 · see citing works

Can a Patent Be Granted for an AI-Generated Invention?

Engel, A. (2020) · GRUR International · doi:10.1093/grurint/ikaa117 · Cited by 11 · see citing works

The New A(I)rt Movement and Its Copyright Protection: Immoral or E-Moral?

Varytimidou, C. (2023) · GRUR International · doi:10.1093/grurint/ikac153 · Cited by 10 · see citing works

Machine Unlearning: its nature, scope, and importance for a "delete culture"Preprint

Floridi, L. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.15242 · Cited by 6 · see citing works

Ever: Mitigating Hallucination in Large Language Models through Real-Time Verification and RectificationPreprint

Kang, H., Ni, J., & Yao, H. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.09114 · Cited by 4 · see citing works

What can we learn from Data Leakage and Unlearning for Law?

Borkar, J. (2023) · arXiv · doi:10.48550/arxiv.2307.10476 · Cited by 4 · see citing works

Large language models in healthcare and medical domain: A review

Nazi, Z. A., & Peng, W. (2024) · Informatics · doi:10.3390/informatics11030057 · Cited by 415 · see citing works

Large Language Models for Cyber Security: A Systematic Literature Review

Xu, H., Wang, S., Li, N., Wang, K., Zhao, Y., Chen, K., Yu, T., Liu, Y., & Wang, H. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3769676 · Cited by 189 · see citing works

Generative AI and deepfakes: a human rights approach to tackling harmful content

Romero Moreno, F. (2024) · International Review of Law, Computers & Technology · doi:10.1080/13600869.2024.2324540 · Cited by 117 · see citing works

Transparent AI Disclosure Obligations: Who, What, When, Where, Why, How

Ali, A. E., Venkatraj, K. P., Morosoli, S., Naudts, L., Helberger, N., & Cesar, P. S. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3650750 · Cited by 51 · see citing works

Generative AI risks and resilience: How users adapt to hallucination and privacy challenges

Lee, C., Kim, J., Lim, J. S., & Shin, D. (2025) · Telematics and Informatics Reports · doi:10.1016/j.teler.2025.100221 · Cited by 53 · see citing works

Elaborating a Human Rights-Friendly Copyright Framework for Generative AI

Geiger, C. (2024) · GRURRR. Gewerblicher Rechtsschutz und Urheberrecht, Rechtsprechungs-Report/GRUR-DVD/GRUR-CD/IIC/Gewerblicher Rechtsschutz und Urheberrecht/Gewerblicher Rechtsschutz und Urheberrecht. Internationaler Teil · doi:10.1007/s40319-024-01481-5 · Cited by 41 · see citing works

Generative AI revolution in cybersecurity: a comprehensive review of threat intelligence and operations

Uddin, M., Irshad, M. S., Kandhro, I. A., Alanazi, F., Ahmed, F., Maaz, M., Hussain, S., & Ullah, S. S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11219-5 · Cited by 55 · see citing works

Large language model for patent concept generation

Ren, R., Ma, J., & Luo, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103301 · Cited by 32 · see citing works

A systematic and bibliometric review of artificial intelligence in sustainable education: Current trends and future research directions

Abulibdeh, A. O. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.101033 · Cited by 34 · see citing works

Technical, legal, and ethical challenges of generative artificial intelligence: an analysis of the governance of training data and copyrights

Pasetti, M., Santos, J. W., Corrêa, N. K., Oliveira, N. F. D., & Barbosa, C. P. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00379-6 · Cited by 17 · see citing works

Biden’s Executive Order on AI and the E.U.’s AI Act: A Comparative Computer-Ethical Analysis

Wörsdörfer, M. (2024) · Philosophy & Technology · doi:10.1007/s13347-024-00765-5 · Cited by 17 · see citing works

Reducing hallucinations of large language models via hierarchical semantic piece

Liu, Y., Yang, Q., Tang, J.-W., Guo, T., Chen, W., Pan, L., Sai, X., Gao, X., Li, Z., Liu, J., et al. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-01833-9 · Cited by 20 · see citing works

Generative AI and data protection

Ruschemeier, H. (2025) · Cambridge Forum on AI: Law and Governance · doi:10.1017/cfl.2024.2 · Cited by 16 · see citing works

Beyond incompatibility: Trade-offs between mutually exclusive fairness criteria in machine learning and law

Zehlike, M., Loosley, A., Jonsson, H., Wiedemann, E., & Hacker, P. (2025) · Artificial Intelligence · doi:10.1016/j.artint.2024.104280 · Cited by 16 · see citing works

AI algorithms under scrutiny: GDPR, DSA, AI Act and CRA as pillars for algorithmic security and privacy in the European Union

Beltrán, M. (2025) · Computers & Security · doi:10.1016/j.cose.2025.104628 · Cited by 12 · see citing works

Biden’s Executive Order on AI: strengths, weaknesses, and possible reform steps

Wörsdörfer, M. (2024) · AI and Ethics · doi:10.1007/s43681-024-00510-w · Cited by 11 · see citing works

Robustness and Cybersecurity in the EU Artificial Intelligence Act

Nolte, H., Rateike, M., & Finck, M. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732020 · Cited by 12 · see citing works

ÜRETİCİ YAPAY ZEKÂ KAYNAKLI NORM VE KİŞİ BAZLI HUKUKİ SORUMLULUK

Yünlü, S. (2024) · Adalet Dergisi · doi:10.57083/adaletdergisi.1484067 · Cited by 9 · see citing works

The legal framework and legal gaps for AI-generated child sexual abuse material

Dushi, D., Bërdufi, N., & Karagianni, A. (2025) · Computer law & security review · doi:10.1016/j.clsr.2025.106205 · Cited by 9 · see citing works

How practitioners can leverage GenAI to bridge the research-practice gap

Lasarov, W., Trabandt, M., Hoffmann, S., & Viglia, G. (2025) · Tourism Management · doi:10.1016/j.tourman.2025.105309 · Cited by 7 · see citing works

Evaluating a Nationally Localized AI Chatbot for Personalized Primary Care Guidance: Insights from the HomeDOCtor Deployment in Slovenia

Gams, M., Horvat, T., Kolar, Ž., Kocuvan, P., Mishev, K., & Simjanoska, M. (2025) · Healthcare · doi:10.3390/healthcare13151843 · Cited by 6 · see citing works

Large language models in healthcare: a systematic evaluation on medical Q/A datasets

Tofeeq, K., Naseer, A., & Wali, A. (2025) · Health Information Science and Systems · doi:10.1007/s13755-025-00397-9 · Cited by 7 · see citing works

Developing a framework for addressing ethical challenges in generative AI

Lucas, S., Heinitz, R. M., Becker, S. J., & Charton, J. E. (2025) · Journal of Information Technology Case and Application Research · doi:10.1080/15228053.2025.2558443 · Cited by 7 · see citing works

GraphRAG-Enabled Local Large Language Model for Gestational Diabetes Mellitus: Development of a Proof-of-Concept

Evangelista, E. D. L., Ruba, F., Bukhari, S., Nazir, A., & Sharma, R. (2025) · JMIR Diabetes · doi:10.2196/76454 · Cited by 5 · see citing works

Examining epistemological challenges of large language models in law

Paseri, L., & Durante, M. (2025) · Cambridge Forum on AI Law and Governance · doi:10.1017/cfl.2024.7 · Cited by 5 · see citing works

Disinformation and Misinformation in the Age of Generative AI

Rudra, K., Ganguly, N., Bonnici, J. P. M., Müller-Budack, E., & Manuvie, R. (2025) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3701551.3705708 · Cited by 3 · see citing works

Decoding accountability: the importance of explainability in liability frameworks for smart border systems

Nnawuchi, U., & George, C. E. (2025) · Discover Computing · doi:10.1007/s10791-025-09559-5 · Cited by 3 · see citing works

Large Language Models in Smart Grid: Applications and Risks

Li, J., Xu, J., Yang, S., & Ngai, E. C. ‐. (2025) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-96146-5_1 · Cited by 3 · see citing works

Transforming European Cybersecurity: AI-Powered Threat Analysis, Quantum Age, Blockchain/Crypto Risks, and Regulatory Strategies

Arslan, R., Özseven, T., & Aydın, M. M. (2025) · International Journal Of Engineering & Applied Sciences · doi:10.24107/ijeas.1619600 · Cited by 3 · see citing works

Legal Horizons of the New Artificial Intelligence Paradigm

Kartskhiya, A. (2024) · Legal Issues in the Digital Age · doi:10.17323/2713-2749.2024.2.4.35 · Cited by 3 · see citing works

Developing a GraphRAG-enabled local-LLM for Gestational Diabetes MellitusPreprint

Evangelista, E. D. L., Ruba, F., Bukhari, S. M. S., Nazir, A., & Sharma, R. S. (2025) · medRxiv · doi:10.1101/2025.04.28.25326568 · Cited by 3 · see citing works

Resultados generados con intervención de sistemas de inteligencia artificial y su protección (o no) por la propiedad intelectual

Llorca, R. E. (2024) · Cuadernos de Derecho Privado · doi:10.62158/cdp.69 · Cited by 2 · see citing works

Security and Privacy for LLMs and LMMs Across Key Sectors: A Literature Survey

Nachuma, C., Fouda, M. M., & Zibran, M. F. (2025) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-89063-5_3 · Cited by 2 · see citing works

Inteligencia artificial generativa: un arma de doble filo para el metaverso

García-Pomareda, J. D. (2024) · Revista e-mercatoria · doi:10.18601/16923960.v23n2.09 · Cited by 2 · see citing works

Guardians of the Digital Realm

Yogi, M. K., Mundru, Y., & Devi, A. M. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-8557-9.ch010 · Cited by 2 · see citing works

Securing the Future: Al-Driven Cybersecurity Solutions for Oil and Gas Industry

Abdi, M., Prasad, P., Balhasan, S., Abdalgader, K., Abdelnabi, A., Hamad, A., Aljazwe, A. B., Magomadov, I. A., Al-Homoud, L., Marei, N., et al. (2025) · SPE Gulf Oil and Gas Technology Conference (GOTECH) · doi:10.2118/224469-ms · Cited by 1 · see citing works

AI-Driven Phishing: Techniques, Threats, and Defence Strategies

Shrestha, L., Balogun, H., & Khan, S. (2025) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-031-82031-1_7 · Cited by 1 · see citing works

ChatGPT, a Life-Changing Phenomenon with Cyber-Security Implications

Danidou, Y. (2025) · Springer eBooks · doi:10.1007/978-3-031-96743-6_14 · Cited by 1 · see citing works

Unboxing the complexity of the AI regulatory sandboxes’ ecosystem: Policy challenges and strategic lines

Longo, E., Bagni, F., & Seferi, F. (2025) · Cambridge Forum on AI Law and Governance · doi:10.1017/cfl.2025.10031 · Cited by 1 · see citing works

Üretken Yapay Zekâ ve Aracı Sorumluluk Rejimi: 5651 Sayılı Kanun'un Yapısal Bağdaşmazlığı ve Reform Seçenekleri

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Judicial Justice and the European Regulation on Artificial Intelligence

Ayuda, F. G. (2024) · Frontiers in Law · doi:10.6000/2817-2302.2024.03.08 · Cited by 1 · see citing works

Generative AI Ethical Dimensions

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G’Sell, F. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4762804

147When EU Law Meets (Large) Language Models

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