AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act

• URL: http://arxiv.org/abs/2406.18211v1
• Date: Wed, 26 Jun 2024 09:51:49 GMT
• Title: AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act
• Authors: Delaram Golpayegani, Isabelle Hupont, Cecilia Panigutti, Harshvardhan J. Pandit, Sven Schade, Declan O'Sullivan, Dave Lewis,
• Abstract summary: Despite its importance, there is a lack of standards and guidelines to assist with drawing up AI and risk documentation aligned with the AI Act. We propose AI Cards as a novel holistic framework for representing a given intended use of an AI system.
• Score: 2.1897070577406734
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the upcoming enforcement of the EU AI Act, documentation of high-risk AI systems and their risk management information will become a legal requirement playing a pivotal role in demonstration of compliance. Despite its importance, there is a lack of standards and guidelines to assist with drawing up AI and risk documentation aligned with the AI Act. This paper aims to address this gap by providing an in-depth analysis of the AI Act's provisions regarding technical documentation, wherein we particularly focus on AI risk management. On the basis of this analysis, we propose AI Cards as a novel holistic framework for representing a given intended use of an AI system by encompassing information regarding technical specifications, context of use, and risk management, both in human- and machine-readable formats. While the human-readable representation of AI Cards provides AI stakeholders with a transparent and comprehensible overview of the AI use case, its machine-readable specification leverages on state of the art Semantic Web technologies to embody the interoperability needed for exchanging documentation within the AI value chain. This brings the flexibility required for reflecting changes applied to the AI system and its context, provides the scalability needed to accommodate potential amendments to legal requirements, and enables development of automated tools to assist with legal compliance and conformity assessment tasks. To solidify the benefits, we provide an exemplar AI Card for an AI-based student proctoring system and further discuss its potential applications within and beyond the context of the AI Act.

Related papers

• Open Problems in Technical AI Governance [93.89102632003996]
  Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
  arXiv  Detail & Related papers  (2024-07-20T21:13:56Z)
• Navigating the EU AI Act: A Methodological Approach to Compliance for Safety-critical Products [0.0]
  This paper presents a methodology for interpreting the EU AI Act requirements for high-risk AI systems. We first propose an extended product quality model for AI systems, incorporating attributes relevant to the Act not covered by current quality models. We then propose a contract-based approach to derive technical requirements at the stakeholder level.
  arXiv  Detail & Related papers  (2024-03-25T14:32:18Z)
• Responsible Artificial Intelligence: A Structured Literature Review [0.0]
  The EU has recently issued several publications emphasizing the necessity of trust in AI. This highlights the urgent need for international regulation. This paper introduces a comprehensive and, to our knowledge, the first unified definition of responsible AI.
  arXiv  Detail & Related papers  (2024-03-11T17:01:13Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• How VADER is your AI? Towards a definition of artificial intelligence systems appropriate for regulation [41.94295877935867]
  Recent AI regulation proposals adopt AI definitions affecting ICT techniques, approaches, and systems that are not AI. We propose a framework to score how validated as appropriately-defined for regulation (VADER) an AI definition is.
  arXiv  Detail & Related papers  (2024-02-07T17:41:15Z)
• AI Hazard Management: A framework for the systematic management of root causes for AI risks [0.0]
  This paper introduces the AI Hazard Management (AIHM) framework. It provides a structured process to systematically identify, assess, and treat AI hazards. It builds upon an AI hazard list from a comprehensive state-of-the-art analysis.
  arXiv  Detail & Related papers  (2023-10-25T15:55:50Z)
• Compliance Challenges in Forensic Image Analysis Under the Artificial Intelligence Act [8.890638003061605]
  We review why the use of machine learning in forensic image analysis is classified as high-risk. Under the draft AI act, high-risk AI systems for use in law enforcement are permitted but subject to compliance with mandatory requirements.
  arXiv  Detail & Related papers  (2022-03-01T14:03:23Z)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
  Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components. There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed. We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
  arXiv  Detail & Related papers  (2020-07-13T20:30:26Z)
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [59.64274607533249]
  AI developers need to make verifiable claims to which they can be held accountable. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
  arXiv  Detail & Related papers  (2020-04-15T17:15:35Z)

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.