Explainable AI (XAI) in Rules as Code (RaC): The DataLex approach

Mowbray, A; Chung, P; Greenleaf, G

Explainable AI (XAI) in Rules as Code (RaC): The DataLex approach

Mowbray, A

Chung, P Greenleaf, G

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Computer Law and Security Review, 2023, 48, pp. 105771
Issue Date:: 2023-04-01

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Field	Value	Language
dc.contributor.author	Mowbray, A https://orcid.org/0000-0002-3667-0706
dc.contributor.author	Chung, P
dc.contributor.author	Greenleaf, G
dc.date.accessioned	2023-09-25T00:37:00Z
dc.date.available	2023-09-25T00:37:00Z
dc.date.issued	2023-04-01
dc.identifier.citation	Computer Law and Security Review, 2023, 48, pp. 105771
dc.identifier.issn	0267-3649
dc.identifier.uri	http://hdl.handle.net/10453/172253
dc.description.abstract	The need for explainability in implementations of ‘Rules as Code (RaC)’ has similarities to the concept of ‘Explainable AI (XAI)’. Explainability is also necessary to avoid RaC being controlled or monopolised by governments and big business. We identify the following desirable features of ‘explainability’ relevant to RaC: Transparency (in various forms); Traceability; Availability; Sustainability; Links to legal sources; and Accountability. Where RaC applications are used to develop automated decision-making systems, some forms of explainability are increasingly likely to be required by law. We then assess how AustLII's DataLex environment implements ‘explainability’ when used to develop RaC: in open software and codebases; in development and maintenance methodologies; and in explanatory features when codebases are executed. All of these XAI aspects of DataLex's RaC are consistent with keeping legislation in the public domain no matter how it is encoded.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Computer Law and Security Review
dc.relation.isbasedon	10.1016/j.clsr.2022.105771
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	1801 Law
dc.subject.classification	Law
dc.subject.classification	4804 Law in context
dc.subject.classification	4806 Private law and civil obligations
dc.title	Explainable AI (XAI) in Rules as Code (RaC): The DataLex approach
dc.type	Journal Article
utslib.citation.volume	48
utslib.for	1801 Law
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Law
utslib.copyright.status	closed_access	*
dc.date.updated	2023-09-25T00:36:59Z
pubs.publication-status	Published
pubs.volume	48

Abstract:

The need for explainability in implementations of ‘Rules as Code (RaC)’ has similarities to the concept of ‘Explainable AI (XAI)’. Explainability is also necessary to avoid RaC being controlled or monopolised by governments and big business. We identify the following desirable features of ‘explainability’ relevant to RaC: Transparency (in various forms); Traceability; Availability; Sustainability; Links to legal sources; and Accountability. Where RaC applications are used to develop automated decision-making systems, some forms of explainability are increasingly likely to be required by law. We then assess how AustLII's DataLex environment implements ‘explainability’ when used to develop RaC: in open software and codebases; in development and maintenance methodologies; and in explanatory features when codebases are executed. All of these XAI aspects of DataLex's RaC are consistent with keeping legislation in the public domain no matter how it is encoded.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/172253