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This paper addresses the challenges of computational accountability in autonomous systems, particularly in Autonomous Vehicles (AVs), where safety and efficiency often conflict. We begin by examining current approaches such as cost minimization, reward maximization, human-centered approaches, and ethical frameworks, noting their limitations addressing these challenges. Foreseeability is a central concept in tort law that limits the accountability and legal liability of an actor to a reasonable scope. Yet, current data-driven methods to determine foreseeability are rigid, ignore uncertainty, and depend on simulation data. In this work, we advocate for a new computational approach to establish foreseeability of autonomous systems based on the legal “BPL” formula. We provide open research challenges, using fully autonomous vehicles as a motivating example, and call for researchers to help autonomous systems make accountable decisions in safety-critical scenarios.
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Accountability as a Foundation for Requirements in Sociotechnical Systems
We understand sociotechnical systems (STSs) as uniting social and technical tiers to provide abstractions for capturing how autonomous principals interact with each other. Accountability is a foundational concept in STSs and an essential component of achieving ethical outcomes. In simple terms, accountability involves identifying who can call whom to account and who must provide an accounting of what and when. Although accountability is essential in any application involving autonomous parties, established methods don't support it. We formulate an accountability requirement as one where one principal is accountable to another regarding some conditional expectation. Our metamodel for STSs captures accountability requirements as relational constructs inspired from legal concepts, such as commitments, authorization, and prohibition. We apply our metamodel to a healthcare process and show how it helps address the problems of ineffective interaction identified in the original case study.
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- Award ID(s):
- 1908374
- PAR ID:
- 10293724
- Date Published:
- Journal Name:
- IEEE Internet Computing
- Volume:
- 25
- Issue:
- 5
- ISSN:
- 1089-7801
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This paper addresses the challenges of computational accountability in autonomous systems, particularly in Autonomous Vehicles (AVs), where safety and efficiency often conflict. We begin by examining current approaches such as cost minimization, reward maximization, human-centered approaches, and ethical frameworks, noting their limitations addressing these challenges. Foreseeability is a central concept in tort law that limits the accountability and legal liability of an actor to a reasonable scope. Yet, current data-driven methods to determine foreseeability are rigid, ignore uncertainty, and depend on simulation data. In this work, we advocate for a new computational approach to establish foreseeability of autonomous systems based on the legal “BPL” formula. We provide open research challenges, using fully autonomous vehicles as a motivating example, and call for researchers to help autonomous systems make accountable decisions in safety-critical scenarios.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/MIC.2021.3106835
