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Littérature scientifique sur le sujet « AI compliance »

Auteur : Grafiati
Publié le 7 juin 2025
Mis à jour le 2 août 2025

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Articles de revues sur le sujet "AI compliance"

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Jackson, Andrew Srivathsan. "Compliance to Autonomous Intentions." Compliance to Autonomous Intentions 10, no. 3 (2025): 931–33. https://doi.org/10.38124/ijisrt/25mar1042.

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Artificial Intelligence (AI) is evolving beyond just following instructions. It’s starting to make decisions in ways that resemble human-like intentions. This paper explores the point where AI stops simply following rules and starts acting on its own, reflecting human intelligence in both predictable and unexpected ways. It also looks at how AI can withhold knowledge, form patterns of behavior, and even develop a subconscious-like intelligence. Using real-world examples, we analyze cases where AI has surprised its creators, raising questions about control, governance, and ethics. This pa
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Bogucka, Edyta, Marios Constantinides, Sanja Šćepanović, and Daniele Quercia. "Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts." Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society 7 (October 16, 2024): 168–80. http://dx.doi.org/10.1609/aies.v7i1.31627.

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In the evolving landscape of AI regulation, it is crucial for companies to conduct impact assessments and document their compliance through comprehensive reports. However, current reports lack grounding in regulations and often focus on specific aspects like privacy in relation to AI systems, without addressing the real-world uses of these systems. Moreover, there is no systematic effort to design and evaluate these reports with both AI practitioners and AI compliance experts. To address this gap, we conducted an iterative co-design process with 14 AI practitioners and 6 AI compliance experts
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Oluwatobi Opeyemi Adeyelu, Chinonye Esther Ugochukwu, and Mutiu Alade Shonibare. "AUTOMATING FINANCIAL REGULATORY COMPLIANCE WITH AI: A REVIEW AND APPLICATION SCENARIOS." Finance & Accounting Research Journal 6, no. 4 (2024): 580–601. http://dx.doi.org/10.51594/farj.v6i4.1035.

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This scholarly paper delves into the transformative realm of Artificial Intelligence (AI) in financial regulatory compliance, offering a classical and engaging exploration of its multifaceted impact. Against an increasingly complex financial landscape backdrop, the study aims to unravel the intricacies of AI integration in compliance models, juxtaposing traditional methodologies with cutting-edge AI-driven approaches. The scope of the paper encompasses a systematic literature review and qualitative analysis, focusing on the evolution of AI in financial services, its necessity for enhanced comp
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Reddy, Haritha Madhava. "Role of AI in Security Compliance." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem32650.

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Abstract—Artificial Intelligence (AI) has emerged as a pivotal tool in enhancing security compliance across various industries. Its ability to analyze vast datasets, detect intricate patterns, and automate complex processes significantly improves risk management and regulatory adherence. AI enables real-time data analysis, promptly identifying potential violations and flagging security threats, thereby strengthening an organization’s overall security framework. However, while AI offers transformative advantages, its integration into existing security systems introduces new challenges, such as
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Bolarinwa Solanke, Femi Bamidele Onita, Obinna Joshua Ochulor, and Henry Oziegbe Iriogbe. "The impact of artificial intelligence on regulatory compliance in the oil and gas industry." International Journal of Science and Technology Research Archive 7, no. 1 (2024): 061–72. http://dx.doi.org/10.53771/ijstra.2024.7.1.0058.

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Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to t
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Onoriode Reginald Aziza, Ngozi Samuel Uzougbo, and Munachi Chikodili Ugwu. "The impact of artificial intelligence on regulatory compliance in the oil and gas industry." World Journal of Advanced Research and Reviews 19, no. 3 (2023): 1559–70. http://dx.doi.org/10.30574/wjarr.2023.19.3.1423.

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Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This abstract examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions t
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Ebube Victor Emeihe, Ejike Innocent Nwankwo, Mojeed Dayo Ajegbile, Janet Aderonke Olaboye, and Chukwudi Cosmos Maha. "The impact of artificial intelligence on regulatory compliance in the oil and gas industry." International Journal of Life Science Research Archive 7, no. 1 (2024): 028–39. http://dx.doi.org/10.53771/ijlsra.2024.7.1.0059.

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Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to t
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Hayes, Kingsley. "Beware the Algorithm: Understanding AI Compliance." ITNOW 64, no. 3 (2022): 58–59. http://dx.doi.org/10.1093/combul/bwac099.

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Abstract The relationship between artificial intelligence (AI) and the General Data Protection Regulation (GDPR) is complex, writes Kingsley Hayes, Partner and Head of Data Breach at Keller Lenkner UK.
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Reddy, Haritha Madhava. "Role of AI in Security Compliance." Journal of Artificial Intelligence & Cloud Computing 3, no. 3 (2024): 1–3. http://dx.doi.org/10.47363/jaicc/2024(3)e194.

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Artificial Intelligence (AI) has emerged as a pivotal tool in enhancing security compliance across various industries. Its ability to analyze vast datasets, detect intricate patterns, and automate complex processes significantly improves risk management and regulatory adherence.
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Doshi, Kinil. "Revolutionizing Compliance with Automation and AI." International Journal of Science, Engineering and Technology 11, no. 5 (2023): 1–7. https://doi.org/10.61463/ijset.vol.11.issue5.567.

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Thèses sur le sujet "AI compliance"

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Taheri, Sojasi Yousef. "Modeling automated legal and ethical compliance for trustworthy AI." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS225.

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Les avancées en intelligence artificielle ont conduit à des enjeux juridiques et éthiques significatifs liés à la vie privée, aux biais, à la responsabilité, etc. Ces dernières années, de nombreuses réglementations ont été mises en place pour limiter ou atténuer les risques associés à l'IA. Le respect de ces réglementations est nécessaire pour la fiabilité des systèmes d'IA et pour garantir une utilisation responsable. De plus, des systèmes d'IA fiables doivent également être éthiques, en assurant une conformité avec les normes éthiques. La conformité aux lois applicables et l'adhésion aux pri
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WIEHLER, Lukas. "How can AI regulation be effectively enforced? : comparing compliance mechanisms for AI regulation with a multiple-criteria decision analysis." Doctoral thesis, European University Institute, 2022. http://hdl.handle.net/1814/74805.

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Award date: 17 June 2022. Supervisor: Professor Andrea Renda (European University Institute)<br>Newly emerging AI regulations need effective and innovative enforcement and compliance mechanisms to assure that fundamental and human rights are protected when using an AI system. This study compares four different compliance mechanisms namely ‘Real-Time and Automated Conformity Assessment’, ‘Standardization and Certification’, ‘Algorithmic Impact Assessment’ and ‘Algorithmic Auditing’ as well as three different assurers of compliance namely deployers, notified bodies and civil society organisation
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FEDERICI, FRANCESCA MARIA. "L'Organismo di Vigilanza ai sensi del d.lgs. 231/2001: compiti e responsabilità." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/262962.

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Oggigiorno, a quasi vent’anni di vigenza del Decreto, nella prassi l’adozione del Modello organizzativo (“MOG” o “Modello”) si è assai diffusa tra le aziende, soprattutto medio-grandi e, specularmente, si è assistito a un florilegio di contestazioni e di condanne, ai sensi del d.lgs. 231/2001. In tale contesto, l’Organismo di Vigilanza (di seguito anche “OdV”) ha da sempre rivestito, secondo l’impianto stesso della normativa, un’imprescindibile funzione. E’ quindi diventato sempre più frequente che professionisti di varia estrazione (per lo più avvocati e commercialisti, ma non solo) affianchi
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PANFILO, DANIELE. "Generating Privacy-Compliant, Utility-Preserving Synthetic Tabular and Relational Datasets Through Deep Learning." Doctoral thesis, Università degli Studi di Trieste, 2022. http://hdl.handle.net/11368/3030920.

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Due tendenze hanno rapidamente ridefinito il panorama dell'intelligenza artificiale (IA) negli ultimi decenni. La prima è il rapido sviluppo tecnologico che rende possibile un'intelligenza artificiale sempre più sofisticata. Dal punto di vista dell'hardware, ciò include una maggiore potenza di calcolo ed una sempre crescente efficienza di archiviazione dei dati. Da un punto di vista concettuale e algoritmico, campi come l'apprendimento automatico hanno subito un'impennata e le sinergie tra l'IA e le altre discipline hanno portato a sviluppi considerevoli. La seconda tendenza è la crescente co
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Livres sur le sujet "AI compliance"

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AI Management System Auditing and Compliance According to the ISO/IEC 42001 Standard: How to Audit, Certify, and Build Responsible AI Systems. Productivity Press, 2024.

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AI Management System Auditing and Compliance According to the ISO/IEC 42001 Standard: How to Audit, Certify, and Build Responsible AI Systems. Productivity Press, 2024.

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AI Management System Auditing and Compliance According to the ISO/IEC 42001 Standard: How to Audit, Certify, and Build Responsible AI Systems. Productivity Press, 2024.

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AI Management System Auditing and Compliance According to the ISO/IEC 42001 Standard: How to Audit, Certify, and Build Responsible AI Systems. Productivity Press, 2024.

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Nicholls, Colin, Alan Baracese, James Maton, Rachel Scott, and John Hatchard. Corruption and Misuse of Public Office. 4th ed. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198907329.001.0001.

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Abstract The 4th edition of this authoritative work has been fully revised and updated. It includes all major developments concerning preventing and combating corruption in England and Wales and globally. Special features include: • Corruption and the impact of the COVID pandemic • New chapter on debarment and the role of the World Bank • Anti-corruption regulation and compliance mechanisms • Implementation and effectiveness of anti-corruption laws and strategies • Impact of AI • Gender dimension of corruption England and Wales coverage: • Bribery Act and the pre-Bribery Act legislation • Deve
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Chapitres de livres sur le sujet "AI compliance"

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Dobrin, Seth. "Compliance Readiness." In AI iQ for a Human-Focused Future. CRC Press, 2024. http://dx.doi.org/10.1201/9781003486725-11.

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Balan, Anil. "Regulatory Compliance in AI Adoption." In AI and Legal Education. Routledge, 2025. https://doi.org/10.4324/9781003607397-6.

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Sayles, James. "Managing and Addressing AI Compliance." In Principles of AI Governance and Model Risk Management. Apress, 2024. https://doi.org/10.1007/979-8-8688-0983-5_8.

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O’Connor, Arthur J. "Regulation and Compliance." In Organizing for Generative AI and the Productivity Revolution. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0959-0_7.

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Pawar, Arun Shrirang, Narendra Singh, Mandar Diwakar, Shivaji B. Jadhav, Osamah Ibrahim Khalaf, and Santosh H. Lavate. "AI-Enhanced Cybersecurity Governance and Compliance." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2124-8_42.

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Libal, Tomer. "Towards Automated GDPR Compliance Checking." In Trustworthy AI - Integrating Learning, Optimization and Reasoning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73959-1_1.

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Zhou, Liyuan, and Hanna Suominen. "Information Extraction to Improve Standard Compliance." In AI 2015: Advances in Artificial Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26350-2_57.

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Westerstrand, Salla. "Fairness in AI Systems Development: Beyond EU AI Act Compliance." In Lecture Notes in Business Information Processing. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85849-9_9.

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Chesani, Federico, Riccardo De Masellis, Chiara Di Francescomarino, et al. "Abducing Compliance of Incomplete Event Logs." In AI*IA 2016 Advances in Artificial Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49130-1_16.

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Pery, Andrew, Majid Rafiei, Michael Simon, and Wil M. P. van der Aalst. "Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities." In Lecture Notes in Business Information Processing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98581-3_29.

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AbstractThe premise of this paper is that compliance with Trustworthy AI governance best practices and regulatory frameworks is an inherently fragmented process spanning across diverse organizational units, external stakeholders, and systems of record, resulting in process uncertainties and in compliance gaps that may expose organizations to reputational and regulatory risks. Moreover, there are complexities associated with meeting the specific dimensions of Trustworthy AI best practices such as data governance, conformance testing, quality assurance of AI model behaviors, transparency, accoun
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Actes de conférences sur le sujet "AI compliance"

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Deshpande, Aniket. "Regulatory Compliance and AI: Navigating the Legal and Regulatory Challenges of AI in Finance." In 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2024. http://dx.doi.org/10.1109/ickecs61492.2024.10616752.

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Werner, John, and Kenneth Arenella. "AI-Powered Compliance: A RAG-Based System for Product Safety Design Engineering." In 2025 IEEE International Symposium on Product Compliance Engineering (ISPCE). IEEE, 2025. https://doi.org/10.1109/ispce64260.2025.11044889.

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Zafar, Shanza Ali, Jessica Kelly, Lena Heidemann, and Núria Mata. "Leveraging Existing Road-Vehicle Standards to Address EU AI Act Compliance." In 2025 IEEE/ACM International Workshop on Responsible AI Engineering (RAIE). IEEE, 2025. https://doi.org/10.1109/raie66699.2025.00017.

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Priyadarshni, Sweta. "AI-Driven Document Automation and Compliance in Contract Lifecycle Management." In 2024 International Conference on Communication, Control, and Intelligent Systems (CCIS). IEEE, 2024. https://doi.org/10.1109/ccis63231.2024.10931892.

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Joshi, Yogi Bhadreshkumar. "AI-Driven Optimization of Hybrid Renewable Energy Systems: Enhancing Efficiency and Reducing Carbon Footprints." In 2025 IEEE International Symposium on Product Compliance Engineering (ISPCE). IEEE, 2025. https://doi.org/10.1109/ispce64260.2025.11044904.

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Joshi, Yogi Bhadreshkumar. "Bridging AI with Product Safety: A Compliance Framework for Battery Storage and Energy Technologies." In 2025 IEEE International Symposium on Product Compliance Engineering (ISPCE). IEEE, 2025. https://doi.org/10.1109/ispce64260.2025.11044886.

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Bunzel, Niklas. "Compliance Made Practical: Translating the EU AI Act into Implementable Security Actions." In 2025 IEEE/ACM International Workshop on Responsible AI Engineering (RAIE). IEEE, 2025. https://doi.org/10.1109/raie66699.2025.00016.

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Chiang, Flore. "Overview of Safety Requirements for Liquid Cooling Technologies in Data Centers Supporting AI and ML Workloads." In 2025 IEEE International Symposium on Product Compliance Engineering (ISPCE). IEEE, 2025. https://doi.org/10.1109/ispce64260.2025.11044900.

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Islam Riad, ABM Kamrul, Md Abdul Barek, Md Mostafizur Rahman, et al. "Enhancing HIPAA Compliance in AI-driven mHealth Devices Security and Privacy." In 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2024. http://dx.doi.org/10.1109/compsac61105.2024.00390.

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Keser, Mert, Youssef Shoeb, and Alois Knoll. "How Could Generative AI Support Compliance with the EU AI Act? A Review for Safe Automated Driving Perception." In 2024 IEEE International Conference on Vehicular Electronics and Safety (ICVES). IEEE, 2024. https://doi.org/10.1109/icves61986.2024.10928135.

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Rapports d'organisations sur le sujet "AI compliance"

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Mittelsteadt, Matthew. AI Verification: Mechanisms to Ensure AI Arms Control Compliance. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20190020.

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The rapid integration of artificial intelligence into military systems raises critical questions of ethics, design and safety. While many states and organizations have called for some form of “AI arms control,” few have discussed the technical details of verifying countries’ compliance with these regulations. This brief offers a starting point, defining the goals of “AI verification” and proposing several mechanisms to support arms inspections and continuous verification.
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Azzutti, Alessio, Mark Cummins, Iain MacNeil, and Chuks Otioma. Simplifying Compliance: The Role of AI and RegTech. University of Glasgow and University of Strathclyde, 2025. https://doi.org/10.36399/gla.pubs.351604.

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The Financial Regulation Innovation Lab (FRIL) is dedicated to simplifying compliance through emerging technologies, with Artificial Intelligence (AI) representing the latest evolution in regulatory technology (RegTech). Building on previous research and industry engagement—including workshops, blogs, webinars, and a micro-credential course—this White Paper presents key considerations for the conceptualisation, design, and implementation of AI-driven compliance systems. We begin by examining the nature of regulatory rules and the compliance process before exploring the complexities that challe
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Bennet, Karen, Gopi Krishnan Rajbahadur, Arthit Suriyawongkul, and Kate Stewart. Implementing AI Bill of Materials (AI BOM) with SPDX 3.0: A Comprehensive Guide to Creating AI and Dataset Bill of Materials. The Linux Foundation, 2024. https://doi.org/10.70828/rned4427.

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A Software Bill of Materials (SBOM) is becoming an increasingly important tool in regulatory and technical spaces to introduce more transparency and security into a project's software supply chain. Artificial intelligence (AI) projects face unique challenges beyond the security of their software, and thus require a more expansive approach to a bill of materials. In this report, we introduce the concept of an AI-BOM, expanding on the SBOM to include the documentation of algorithms, data collection methods, frameworks and libraries, licensing information, and standard compliance.
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Otioma, Chuks, and Iain MacNeil. Robo Advisors as a Use Case of AI Systems: Linking Responsible Business Practices, Compliance and Outcomes. University of Glasgow and University of Strathclyde, 2025. https://doi.org/10.36399/gla.pubs.351605.

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In this paper, we explore the workings of robo-advisors as an example of AI-based systems. We discuss the performance and challenges of robo-advice, as well as offer reflections on how and why robo-advice as part of the broader fintech and financial services sector intersects practices in AI systems, regulation and compliance. We draw attention to the implications for explainable AI, the role of humans in the loop, compliance and business practices. Our approach focuses on how the AI capabilities in robo-advisors can help to build responsible business practices and compliance elements into ope
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Wagner, Rudolf. EU AI Act (2024/1689) and EU MDR (2017/745): Breaking the Expensive Myth: Why AI-Powered Medical Devices Under EU MDR Don’t Need EU AI Act Certification – A Detailed Analysis of Regulatory Requirements and Compliance. Rudolf Wagner, 2025. https://doi.org/10.70317/2025.02rw02.

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The Growing Fear of Regulatory Overlap in AI-Driven Healthcare The EU AI Act (Regulation (EU) 2024/1689) has ignited discussions across the healthcare industry, especially among manufacturers of AI-powered medical devices. A major concern is whether these devices, already scrutinized under the EU Medical Device Regulation (MDR) 2017/745, will now face an additional regulatory burden—a second certification under the AI Act. This would mean double the compliance costs, twice the paperwork, and unnecessary delays for market entry. This paper shows that double certification is redundant and is not
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Pasupuleti, Murali Krishna. Automated Smart Contracts: AI-powered Blockchain Technologies for Secure and Intelligent Decentralized Governance. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv425.

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Abstract: Automated smart contracts represent a paradigm shift in decentralized governance by integrating artificial intelligence (AI) with blockchain technologies to enhance security, scalability, and adaptability. Traditional smart contracts, while enabling trustless and automated transactions, often lack the flexibility to adapt to dynamic regulatory frameworks, evolving economic conditions, and real-time security threats. AI-powered smart contracts leverage machine learning, reinforcement learning, and predictive analytics to optimize contract execution, detect fraudulent transactions, and
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Tjondronegoro, Dr Dian Wirawan. Navigating AI risks for sustainable productivity and resilience. Asian Productivity Organization, 2025. https://doi.org/10.61145/occa6400.

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In Navigating AI Risks for Sustainable Productivity and Resilience, Dr. Dian Wirawan Tjondronegoro presents a structured framework for integrating artificial intelligence (AI) into organizations while mitigating associated risks. The report outlines three interdependent frameworks—EPIC (Education, Partnership, Infrastructure, Community), TOAST (Trustworthy, Optimised, Adaptable, Socio-Technological), and RAIIF (Responsible AI Implementation Framework)—which collectively support AI readiness, responsible governance, and continuous adaptation. Emphasizing the socio-technical nature of AI, the pu
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Borrett, Veronica, Melissa Hanham, Gunnar Jeremias, et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.

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The integration of novel technologies for monitoring and investigating compliance can enhance the effectiveness of regimes related to weapons of mass destruction (WMD). This report looks at the potential role of four novel approaches based on recent technological advances – remote sensing tools; open-source satellite data; open-source trade data; and artificial intelligence (AI) – in monitoring and investigating compliance with WMD treaties. The report consists of short essays from leading experts that introduce particular technologies, discuss their applications in WMD regimes, and consider s
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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats,
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Wagner, Rudolf. Enforcing Software and AI as Medical Devices: Expert Witness Insights on Civil Lawsuits, Regulation, and Legal Liability Pathways. ADHOCON UG (haftungsbeschränkt), 2024. http://dx.doi.org/10.70317/2024.13rw10.

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As an expert witness to German courts (Landgerichte, Oberlandesgerichte) and federal courts for Medical Devices and IVDs the number of expert statements has constantly increased, especially for Software and AI in Healthcare and focusing on their Medical Device classification. The paper also examines how competition and insurers may respond to claims arising from the use of improper classified software, artificial intelligence including Large Language Models (LLMs) with denial of coverage, increased premiums, and subrogation actions against hospitals or AI developers. Regulatory challenges are
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