75 research outputs found
Spent convictions and the architecture for establishing legal semantic workflows
This research was partially funded by the Data to Decisions Cooperative Research Centre (D2D CRC, Australia), and Meta-Rule of Law (DER2016- 78108-P, Spain)Operating within the Data to Decision Cooperative Research Centre (D2D CRC), the authors are currently involved in the Integrated Law Enforcement program and the Compliance through Design project. These have the goal of developing a federated data platform for law enforcement agencies that will enable the execution of integrated analytics on data accessed from different external and internal sources, thereby providing effective support to an investigator or analyst working to evaluate evidence and manage lines of inquiries in an investigation. Technical solutions should also operate ethically, in compliance with the law and subject to good governance principles. This paper is focused on the Australian spent convictions scheme, which provide use cases to test the platform
On annotation of the textual contents of Scottish legal instruments
We thank the funding from the University of Aberdeen’s Impact, Knowledge Exchange, and Commercialisation Award for this 10 week study. This work was also supported by the French National Research Agency (ANR-10-LABX-0083) in the context of the Labex EFL. We also thank the student staff: A. Andonov, A. Faulds, E. Onwa, L. Schelling, R. Stoyanov, and O. Toloch.Publisher PD
Modelling and accessing regulatory knowledge for computer-assisted compliance audit
The ingredients for an effective automated audit of a building design include a building model containing the design information, a computerised regulatory knowledge model, and a practical method of processing these computable representations. There have been numerous approaches to computer-aided compliance audit in the AEC/FM domain over the last four decades, but none has yet evolved into a practical solution. One reason is that they have all been isolated attempts that lack any form of industry-wide standardisation. The current research project, therefore, focuses on investigating the use of the industry standard building information model and the adoption of open standard legal knowledge interchange and executable workflow models for automating conventional compliant design processes. This paper provides a non-exhaustive overview of common approaches to model and access regulatory knowledge for a compliance audit. The strengths and weaknesses of two comparative open standard knowledge representation approaches are discussed using an example regulatory document
Compliance checking in reified IO logic via SHACL
Reified Input/Output (I/O) logic[21] has been recently proposed to model real-world norms in terms of the logic in [11]. This is massively grounded on the notion of reification, and it has specifically designed to model meaning of natural language sentences, such as the ones occurring in existing legislation. This paper presents a methodology to carry out compliance checking on reified I/O logic formulae. These are translated in SHACL (Shapes Constraint Language) shapes, a recent W3C recommendation to validate and reason with RDF triplestores. Compliance checking is then enforced by validating RDF graphs describing states of affairs with respect to these SHACL shapes
Machine Understandable Policies and GDPR Compliance Checking
The European General Data Protection Regulation (GDPR) calls for technical
and organizational measures to support its implementation. Towards this end,
the SPECIAL H2020 project aims to provide a set of tools that can be used by
data controllers and processors to automatically check if personal data
processing and sharing complies with the obligations set forth in the GDPR. The
primary contributions of the project include: (i) a policy language that can be
used to express consent, business policies, and regulatory obligations; and
(ii) two different approaches to automated compliance checking that can be used
to demonstrate that data processing performed by data controllers / processors
complies with consent provided by data subjects, and business processes comply
with regulatory obligations set forth in the GDPR
Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering
intelligent decision-making and a wide range of Artificial Intelligence (AI)
services across major corporations such as Google, Walmart, and AirBnb. KGs
complement Machine Learning (ML) algorithms by providing data context and
semantics, thereby enabling further inference and question-answering
capabilities. The integration of KGs with neuronal learning (e.g., Large
Language Models (LLMs)) is currently a topic of active research, commonly named
neuro-symbolic AI. Despite the numerous benefits that can be accomplished with
KG-based AI, its growing ubiquity within online services may result in the loss
of self-determination for citizens as a fundamental societal issue. The more we
rely on these technologies, which are often centralised, the less citizens will
be able to determine their own destinies. To counter this threat, AI
regulation, such as the European Union (EU) AI Act, is being proposed in
certain regions. The regulation sets what technologists need to do, leading to
questions concerning: How can the output of AI systems be trusted? What is
needed to ensure that the data fuelling and the inner workings of these
artefacts are transparent? How can AI be made accountable for its
decision-making? This paper conceptualises the foundational topics and research
pillars to support KG-based AI for self-determination. Drawing upon this
conceptual framework, challenges and opportunities for citizen
self-determination are illustrated and analysed in a real-world scenario. As a
result, we propose a research agenda aimed at accomplishing the recommended
objectives
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