Legal Provocations for HCI in the Design and Development of Trustworthy Autonomous Systems

• URL: http://arxiv.org/abs/2206.07506v1
• Date: Wed, 15 Jun 2022 13:03:43 GMT
• Title: Legal Provocations for HCI in the Design and Development of Trustworthy Autonomous Systems
• Authors: Lachlan D. Urquhart, Glenn McGarry and Andy Crabtree
• Abstract summary: We consider a series of legal provocations emerging from the proposed European Union AI Act 2021 (AIA) AIA targets AI developments that pose risks to society and citizens fundamental rights, introducing mandatory design and development requirements for high-risk AI systems (HRAIS) These requirements open up new opportunities for HCI that reach beyond established concerns with the ethics and explainability of AI and situate AI development in human-centered processes and methods of design to enable compliance with regulation and foster societal trust in AI.
• Score: 2.575172714412997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider a series of legal provocations emerging from the proposed European Union AI Act 2021 (AIA) and how they open up new possibilities for HCI in the design and development of trustworthy autonomous systems. The AIA continues the by design trend seen in recent EU regulation of emerging technologies. The AIA targets AI developments that pose risks to society and citizens fundamental rights, introducing mandatory design and development requirements for high-risk AI systems (HRAIS). These requirements regulate different stages of the AI development cycle including ensuring data quality and governance strategies, mandating testing of systems, ensuring appropriate risk management, designing for human oversight, and creating technical documentation. These requirements open up new opportunities for HCI that reach beyond established concerns with the ethics and explainability of AI and situate AI development in human-centered processes and methods of design to enable compliance with regulation and foster societal trust in AI.

Related papers

• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• 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)
• 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)
• Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
  General purpose AI seems to have lowered the barriers for the public to use AI and harness its power. We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
  arXiv  Detail & Related papers  (2024-03-21T19:12:37Z)
• The European Commitment to Human-Centered Technology: The Integral Role of HCI in the EU AI Act's Success [4.202570851109354]
  The EU has enacted the AI Act, regulating market access for AI-based systems. The Act focuses regulation on transparency, explainability, and the human ability to understand and control AI systems. The EU issues a democratic call for human-centered AI systems and, in turn, an interdisciplinary research agenda for human-centered innovation in AI development.
  arXiv  Detail & Related papers  (2024-02-22T17:35:29Z)
• 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)
• 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)
• Responsible Design Patterns for Machine Learning Pipelines [10.184056098238765]
  AI ethics involves applying ethical principles to the entire life cycle of AI systems. This is essential to mitigate potential risks and harms associated with AI, such as biases. To achieve this goal, responsible design patterns (RDPs) are critical for Machine Learning (ML) pipelines.
  arXiv  Detail & Related papers  (2023-05-31T15:47:12Z)
• 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)
• 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.