Handling inconsistent and uncertain legal reasoning for AI vehicles design

Yiwei Lu; Yuhui Lin; Burkhard Schafer; Andrew Ireland; Lachlan Urquhart; Zhe Yu

doi:10.48550/arXiv.2305.12203

Handling inconsistent and uncertain legal reasoning for AI vehicles design

Yiwei Lu^*, Yuhui Lin, Burkhard Schafer, Andrew Ireland, Lachlan Urquhart, Zhe Yu

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

As AI products continue to evolve, increasingly legal problems are emerging for the engineers that design them. For example, if the aim is to build an autonomous vehicle (AV) that adheres to current laws, should we give it the ability to ignore a red traffic light in an emergency, or is this merely an excuse we permit humans to male? The paper argues that some of the changes brought by AVs are best understood as necessitating a revision of law’s ontology. Current laws are often ambiguous, inconsistent or undefined when it comes to technologies that make use of AI. Engineers would benefit from decision support tools that provide engineer’s with legal advice and guidance on their design decisions.This research aims at exploring a new representation of legal ontology by importing argumentation theory and constructing a trustworthy legal decision system.While the ideas are generally applicable to AI products, our initial focus has been on Autonomous Vehicles (AVs)

Original language	English
Title of host publication	Proceedings of the International Workshop on Methodologies for Translating Legal Norms into Formal Representations (LN2FR 2022)
Editors	Ken Satoh, Georg Borges, Erich Schweighofer
Publisher	ArXiv
Pages	76-89
DOIs	https://doi.org/10.48550/arXiv.2305.12203
Publication status	Published - 20 May 2023

Publication series

Name	Logic in Computing

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10.48550/arXiv.2305.12203Licence: Creative Commons: Attribution (CC-BY)

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Lu, Y., Lin, Y., Schafer, B., Ireland, A., Urquhart, L., & Yu, Z. (2023). Handling inconsistent and uncertain legal reasoning for AI vehicles design. In K. Satoh, G. Borges, & E. Schweighofer (Eds.), Proceedings of the International Workshop on Methodologies for Translating Legal Norms into Formal Representations (LN2FR 2022) (pp. 76-89). (Logic in Computing). ArXiv. https://doi.org/10.48550/arXiv.2305.12203

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title = "Handling inconsistent and uncertain legal reasoning for AI vehicles design",

abstract = "As AI products continue to evolve, increasingly legal problems are emerging for the engineers that design them. For example, if the aim is to build an autonomous vehicle (AV) that adheres to current laws, should we give it the ability to ignore a red traffic light in an emergency, or is this merely an excuse we permit humans to male? The paper argues that some of the changes brought by AVs are best understood as necessitating a revision of law{\textquoteright}s ontology. Current laws are often ambiguous, inconsistent or undefined when it comes to technologies that make use of AI. Engineers would benefit from decision support tools that provide engineer{\textquoteright}s with legal advice and guidance on their design decisions.This research aims at exploring a new representation of legal ontology by importing argumentation theory and constructing a trustworthy legal decision system.While the ideas are generally applicable to AI products, our initial focus has been on Autonomous Vehicles (AVs)",

author = "Yiwei Lu and Yuhui Lin and Burkhard Schafer and Andrew Ireland and Lachlan Urquhart and Zhe Yu",

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Lu, Y, Lin, Y, Schafer, B, Ireland, A, Urquhart, L & Yu, Z 2023, Handling inconsistent and uncertain legal reasoning for AI vehicles design. in K Satoh, G Borges & E Schweighofer (eds), Proceedings of the International Workshop on Methodologies for Translating Legal Norms into Formal Representations (LN2FR 2022). Logic in Computing, ArXiv, pp. 76-89. https://doi.org/10.48550/arXiv.2305.12203

Handling inconsistent and uncertain legal reasoning for AI vehicles design. / Lu, Yiwei; Lin, Yuhui; Schafer, Burkhard et al.
Proceedings of the International Workshop on Methodologies for Translating Legal Norms into Formal Representations (LN2FR 2022). ed. / Ken Satoh; Georg Borges; Erich Schweighofer. ArXiv, 2023. p. 76-89 (Logic in Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Handling inconsistent and uncertain legal reasoning for AI vehicles design

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AU - Lin, Yuhui

AU - Schafer, Burkhard

AU - Ireland, Andrew

AU - Urquhart, Lachlan

AU - Yu, Zhe

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AB - As AI products continue to evolve, increasingly legal problems are emerging for the engineers that design them. For example, if the aim is to build an autonomous vehicle (AV) that adheres to current laws, should we give it the ability to ignore a red traffic light in an emergency, or is this merely an excuse we permit humans to male? The paper argues that some of the changes brought by AVs are best understood as necessitating a revision of law’s ontology. Current laws are often ambiguous, inconsistent or undefined when it comes to technologies that make use of AI. Engineers would benefit from decision support tools that provide engineer’s with legal advice and guidance on their design decisions.This research aims at exploring a new representation of legal ontology by importing argumentation theory and constructing a trustworthy legal decision system.While the ideas are generally applicable to AI products, our initial focus has been on Autonomous Vehicles (AVs)

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Lu Y, Lin Y, Schafer B, Ireland A, Urquhart L, Yu Z. Handling inconsistent and uncertain legal reasoning for AI vehicles design. In Satoh K, Borges G, Schweighofer E, editors, Proceedings of the International Workshop on Methodologies for Translating Legal Norms into Formal Representations (LN2FR 2022). ArXiv. 2023. p. 76-89. (Logic in Computing). doi: 10.48550/arXiv.2305.12203

Handling inconsistent and uncertain legal reasoning for AI vehicles design

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