The risks of risk-based AI regulation: taking liability seriously

• URL: http://arxiv.org/abs/2311.14684v1
• Date: Fri, 3 Nov 2023 12:51:37 GMT
• Title: The risks of risk-based AI regulation: taking liability seriously
• Authors: Martin Kretschmer, Tobias Kretschmer, Alexander Peukert, Christian Peukert
• Abstract summary: The development and regulation of AI seems to have reached a critical stage. Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4. This paper analyses the most advanced legal proposal, the European Union's AI Act.
• Score: 46.90451304069951
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development and regulation of multi-purpose, large "foundation models" of AI seems to have reached a critical stage, with major investments and new applications announced every other day. Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4. Legislators globally compete to set the blueprint for a new regulatory regime. This paper analyses the most advanced legal proposal, the European Union's AI Act currently in the stage of final "trilogue" negotiations between the EU institutions. This legislation will likely have extra-territorial implications, sometimes called "the Brussels effect". It also constitutes a radical departure from conventional information and communications technology policy by regulating AI ex-ante through a risk-based approach that seeks to prevent certain harmful outcomes based on product safety principles. We offer a review and critique, specifically discussing the AI Act's problematic obligations regarding data quality and human oversight. Our proposal is to take liability seriously as the key regulatory mechanism. This signals to industry that if a breach of law occurs, firms are required to know in particular what their inputs were and how to retrain the system to remedy the breach. Moreover, we suggest differentiating between endogenous and exogenous sources of potential harm, which can be mitigated by carefully allocating liability between developers and deployers of AI technology.

Related papers

• AI threats to national security can be countered through an incident regime [55.2480439325792]
  We propose a legally mandated post-deployment AI incident regime that aims to counter potential national security threats from AI systems. Our proposed AI incident regime is split into three phases. The first phase revolves around a novel operationalization of what counts as an 'AI incident' The second and third phases spell out that AI providers should notify a government agency about incidents, and that the government agency should be involved in amending AI providers' security and safety procedures.
  arXiv  Detail & Related papers  (2025-03-25T17:51:50Z)
• In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI [93.33036653316591]
  We call for three interventions to advance system safety. First, we propose using standardized AI flaw reports and rules of engagement for researchers. Second, we propose GPAI system providers adopt broadly-scoped flaw disclosure programs. Third, we advocate for the development of improved infrastructure to coordinate distribution of flaw reports.
  arXiv  Detail & Related papers  (2025-03-21T05:09:46Z)
• Compliance of AI Systems [0.0]
  This paper systematically examines the compliance of AI systems with relevant legislation, focusing on the EU's AI Act. The analysis highlighted many challenges associated with edge devices, which are increasingly being used to deploy AI applications closer and closer to the data sources. The importance of data set compliance is highlighted as a cornerstone for ensuring the trustworthiness, transparency, and explainability of AI systems.
  arXiv  Detail & Related papers  (2025-03-07T16:53:36Z)
• Securing the AI Frontier: Urgent Ethical and Regulatory Imperatives for AI-Driven Cybersecurity [0.0]
  This paper critically examines the evolving ethical and regulatory challenges posed by the integration of artificial intelligence in cybersecurity. We trace the historical development of AI regulation, highlighting major milestones from theoretical discussions in the 1940s to the implementation of recent global frameworks such as the European Union AI Act. Ethical concerns such as bias, transparency, accountability, privacy, and human oversight are explored in depth, along with their implications for AI-driven cybersecurity systems.
  arXiv  Detail & Related papers  (2025-01-15T18:17:37Z)
• Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
  This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act) Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence. As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
  arXiv  Detail & Related papers  (2024-10-10T17:38:38Z)
• How Could Generative AI Support Compliance with the EU AI Act? A Review for Safe Automated Driving Perception [4.075971633195745]
  Deep Neural Networks (DNNs) have become central for the perception functions of autonomous vehicles. The European Union (EU) Artificial Intelligence (AI) Act aims to address these challenges by establishing stringent norms and standards for AI systems. This review paper summarizes the requirements arising from the EU AI Act regarding DNN-based perception systems and systematically categorizes existing generative AI applications in AD.
  arXiv  Detail & Related papers  (2024-08-30T12:01:06Z)
• An FDA for AI? Pitfalls and Plausibility of Approval Regulation for Frontier Artificial Intelligence [0.0]
  We explore the applicability of approval regulation -- that is, regulation of a product that combines experimental minima with government licensure conditioned partially or fully upon that experimentation -- to the regulation of frontier AI. There are a number of reasons to believe that approval regulation, simplistically applied, would be inapposite for frontier AI risks. We conclude by highlighting the role of policy learning and experimentation in regulatory development.
  arXiv  Detail & Related papers  (2024-08-01T17:54:57Z)
• Near to Mid-term Risks and Opportunities of Open-Source Generative AI [94.06233419171016]
  Applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about potential risks and resulted in calls for tighter regulation. This regulation is likely to put at risk the budding field of open-source Generative AI.
  arXiv  Detail & Related papers  (2024-04-25T21:14:24Z)
• Regulating Chatbot Output via Inter-Informational Competition [8.168523242105763]
  This Article develops a yardstick for reevaluating both AI-related content risks and corresponding regulatory proposals. It argues that sufficient competition among information outlets in the information marketplace can sufficiently mitigate and even resolve most content risks posed by generative AI technologies.
  arXiv  Detail & Related papers  (2024-03-17T00:11:15Z)
• A Safe Harbor for AI Evaluation and Red Teaming [124.89885800509505]
  Some researchers fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. We propose that major AI developers commit to providing a legal and technical safe harbor. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.
  arXiv  Detail & Related papers  (2024-03-07T20:55:08Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• Regulation and NLP (RegNLP): Taming Large Language Models [51.41095330188972]
  We argue how NLP research can benefit from proximity to regulatory studies and adjacent fields. We advocate for the development of a new multidisciplinary research space on regulation and NLP.
  arXiv  Detail & Related papers  (2023-10-09T09:22:40Z)
• AI Regulation in Europe: From the AI Act to Future Regulatory Challenges [3.0821115746307663]
  It argues for a hybrid regulatory strategy that combines elements from both philosophies. The paper examines the AI Act as a pioneering legislative effort to address the multifaceted challenges posed by AI. It advocates for immediate action to create protocols for regulated access to high-performance, potentially open-source AI systems.
  arXiv  Detail & Related papers  (2023-10-06T07:52:56Z)
• Quantitative study about the estimated impact of the AI Act [0.0]
  We suggest a systematic approach that we applied on the initial draft of the AI Act that has been released in April 2021. We went through several iterations of compiling the list of AI products and projects in and from Germany, which the Lernende Systeme platform lists. It turns out that only about 30% of the AI systems considered would be regulated by the AI Act, the rest would be classified as low-risk.
  arXiv  Detail & Related papers  (2023-03-29T06:23:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.