Liability regimes in the age of AI: a use-case driven analysis of the burden of proof

• URL: http://arxiv.org/abs/2211.01817v1
• Date: Thu, 3 Nov 2022 13:55:36 GMT
• Title: Liability regimes in the age of AI: a use-case driven analysis of the burden of proof
• Authors: David Fern\'andez Llorca, Vicky Charisi, Ronan Hamon, Ignacio S\'anchez, Emilia G\'omez
• Abstract summary: New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. But there is growing concerns about certain intrinsic characteristics of these methodologies that carry potential risks to both safety and fundamental rights. This paper presents three case studies, as well as the methodology to reach them, that illustrate these difficulties.
• Score: 1.7510020208193926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. In particular, data-driven learning approaches (i.e., Machine Learning (ML)) have been a true revolution in the advancement of multiple technologies in various application domains. But at the same time there is growing concerns about certain intrinsic characteristics of these methodologies that carry potential risks to both safety and fundamental rights. Although there are mechanisms in the adoption process to minimize these risks (e.g., safety regulations), these do not exclude the possibility of harm occurring, and if this happens, victims should be able to seek compensation. Liability regimes will therefore play a key role in ensuring basic protection for victims using or interacting with these systems. However, the same characteristics that make AI systems inherently risky, such as lack of causality, opacity, unpredictability or their self and continuous learning capabilities, lead to considerable difficulties when it comes to proving causation. This paper presents three case studies, as well as the methodology to reach them, that illustrate these difficulties. Specifically, we address the cases of cleaning robots, delivery drones and robots in education. The outcome of the proposed analysis suggests the need to revise liability regimes to alleviate the burden of proof on victims in cases involving AI technologies.

Related papers

• Threats, Attacks, and Defenses in Machine Unlearning: A Survey [15.05662521329346]
  Machine Unlearning (MU) has gained considerable attention recently for its potential to achieve Safe AI. This survey aims to fill the gap between the extensive number of studies on threats, attacks, and defenses in machine unlearning.
  arXiv  Detail & Related papers  (2024-03-20T15:40:18Z)
• Control Risk for Potential Misuse of Artificial Intelligence in Science [85.91232985405554]
  We aim to raise awareness of the dangers of AI misuse in science. We highlight real-world examples of misuse in chemical science. We propose a system called SciGuard to control misuse risks for AI models in science.
  arXiv  Detail & Related papers  (2023-12-11T18:50:57Z)
• 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)
• Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities [4.466887678364242]
  Motivated by the need to address the security concerns in AI-Robotics systems, this paper presents a comprehensive survey and taxonomy across three dimensions. We begin by surveying potential attack surfaces and provide mitigating defensive strategies. We then delve into ethical issues, such as dependency and psychological impact, as well as the legal concerns regarding accountability for these systems.
  arXiv  Detail & Related papers  (2023-10-12T17:54:20Z)
• Predictable Artificial Intelligence [67.79118050651908]
  We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems. This paper aims to elucidate the questions, hypotheses and challenges relevant to Predictable AI.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity [18.324118502535775]
  We argue that human-AI teaming is worthwhile in cybersecurity. We emphasize the importance of a balanced approach that incorporates AI's computational power with human expertise.
  arXiv  Detail & Related papers  (2023-09-28T01:20:44Z)
• Evaluating the Vulnerabilities in ML systems in terms of adversarial attacks [0.0]
  New adversarial attacks methods may pose challenges to current deep learning cyber defense systems. Authors explore the consequences of vulnerabilities in AI systems. It is important to train the AI systems appropriately when they are in testing phase and getting them ready for broader use.
  arXiv  Detail & Related papers  (2023-08-24T16:46:01Z)
• Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety [54.478842696269304]
  The use of deep neural networks (DNNs) in safety-critical applications is challenging due to numerous model-inherent shortcomings. In recent years, a zoo of state-of-the-art techniques aiming to address these safety concerns has emerged. Our paper addresses both machine learning experts and safety engineers.
  arXiv  Detail & Related papers  (2021-04-29T09:54:54Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)
• 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.