Normative Autonomous Vehicles
A workshop in conjunction with the 20th International Conference on AI and Law (ICAIL'25)
Northwestern University, Chicago, USA
June 16 to 20, 2025
Important Dates
* Paper submission deadline: 1 May 2025
* Acceptance Notification: 12 May 2025
* Camera Ready submission: 26 May 2025
Autonomously
driven vehicles (AVs) and humans are poised to share the roads for the
foreseeable future, leading to complex interactions between AVs, human
driven vehicles (HVs), pedestrians, etc. To ensure road safety and
coordination, AVs and HVs must adhere to shared legal frameworks
encompassing the rules of the road, the duty of care, and liability
considerations. This shared framework requires autonomous and human
agents to anticipate and cooperate with respect to one another’s actions
and responsibilities, fostering an environment of mutual understanding
and accountability.
The
foundational assumption is that a high-level, universally applicable
model of traffic rules should govern both humans and machines. This
model would enable both parties to draw similar inferences, maintain
consistent expectations, and behave in compatible ways on shared roads.
At
this workshop, we will delve into the issue of representation of
traffic rules and driving conduct in ways that enables machines to
execute them autonomously, in a way that is understandable and
explainable to humans, so that the authorities can verify compliance. A
key tenet of this discussion is that liability and negligence should be
applied uniformly to all road users. Human drivers should not be
unfairly penalized due to an AV’s superior perceptual and computational
capabilities.
This
focus on traffic rules arises from their practical importance—human
drivers are required to explicitly learn and demonstrate mastery of
these rules. Representing these rules for machines, however, is fraught
with challenges, including gaps in data, highly parameterized contexts,
commonsense, dependencies on situational factors, and ambiguities
inherent in legal language. For example, concepts like “safe gap” are
inherently open-textured and context-dependent, while object modeling
(e.g., understanding long trucks) and mental models of other road users
add layers of complexity. Additionally, the prescriptive nature of
traffic rules, combined with their interplay with broader legal
principles like liability and negligence, further complicates the task.
These issues open multiple, challenging, and intriguing lines of
research.
The
use of symbolic logic to formalize legal norms should be part of such a
system, as such approaches hold promise for the automation of legal
reasoning. Significant challenges are still open in the field of logic
rule modelling, such as the acquisition bottleneck, but new
technological advancements can assist in these tasks. One such
advancement is the use of language models to automatically parse the
natural language in a logic representation in a formal language and to
validate the correctness of the resulting logic structure. Other
developments should be brought to the fore.
The
workshop aims to tackle these challenges by focusing on the use of
formal languages and models to represent traffic rules in
machine-readable formats as well as the integration of Large Language
Models to aid the extraction of rules and regulations. Furthermore, the
workshop aims to showcase the different approaches to the issue and
their use in applying legal reasoning to the AV behaviour.
Workshop Topics
The
workshop seeks to foster discussions on a broad range of topics,
including but not limited to the following and with reference to
autonomous vehicles:
*
Knowledge Representation Methods: Exploration of techniques suitable
for representing legal norms (especially traffic rules), including
deontic logic, first-order logic, and other comparable formalisms.
*Rule-Exceptions,
Conflicts, and Contrary-to-Duty Obligations: Formalization and
reasoning approaches to address exceptions to rules, conflicting
regulations, violations, and secondary obligations or prohibitions that
arise when other deontic specifications are violated.
*
Abstract Legal Concepts and Principles: Representation of foundational
legal principles such as "human dignity," "mutual respect," "care,"
"trust," and "danger."
*
Practical Implementation of Formalisations of the Law: Development and
application of tools such as legal knowledge bases, ontologies,
reasoning engines, and SAT-solvers to operationalize formal
representations.
*
Translation of Legal Texts to Formal Representations: Methods—both
automated and manual—for converting natural-language legal provisions
into formal languages, focusing on traffic rules.
*
Legal and Engineering Challenges: Examination of the technical and
legal hurdles associated with applying formal representations to
real-world systems, especially in the context of traffic laws.
*
AI Compliance and Ethical Reasoning: Techniques for enabling AI systems
to reason ethically and comply with traffic rules through formalized
legal knowledge.
*
Machine Learning and Hybrid Approaches: Integration of machine learning
methods for knowledge extraction and hybrid symbolic/sub-symbolic
approaches for reasoning with formalized traffic rules.
*
Legal and Liability Concepts: Discussion on responsibility allocation
for rule violations and the implications for sanctions and liability in
mixed-traffic environments.
*
Interface Between Code and Normative Rules: Exploration of how legal
rules can be embedded into code and the challenges of ensuring accuracy
and fairness.
*
Verification and Validation of Rule Bases: Ensuring that formalized
rules are correct, complete, and aligned with legal norms.
*
Cognitive Models of Driving: Analysis of mental models that guide human
drivers and how these can inform the design of AV systems.
*
Use Cases and Demos: Presentation of practical examples and
demonstrations showcasing the application of formalized legal norms in
automated systems.
*
Philosophical Considerations: Discussions on meaningful human control,
particularly in terms of interaction between humans and autonomous
agents.
* Corpora: textual corpora which has been analysed and evaluated using computational linguistic techniques.
The
workshop particularly invites submissions featuring experience reports
on implementing legal norm formalizations in automated systems, offering
valuable insights into practical challenges and solutions.
Organisers
Adam Wyner
-
-
Associate Professor of Computer Science, Department of Computer Science, Swansea University, United Kingdom
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a.z....@swansea.ac.uk
Galileo Sartor