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Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination

Authors: Luis-Daniel Ibáñez, John Domingue, Sabrina Kirrane, Oshani Seneviratne, Aisling Third, and Maria-Esther Vidal

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract

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 the output of AI systems can 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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Luis-Daniel Ibáñez, John Domingue, Sabrina Kirrane, Oshani Seneviratne, Aisling Third, and Maria-Esther Vidal. Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 9:1-9:32, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{ibanez_et_al:TGDK.1.1.9,
  author =	{Ib\'{a}\~{n}ez, Luis-Daniel and Domingue, John and Kirrane, Sabrina and Seneviratne, Oshani and Third, Aisling and Vidal, Maria-Esther},
  title =	{{Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{9:1--9:32},
  ISSN =	{2942-7517},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.9},
  URN =		{urn:nbn:de:0030-drops-194839},
  doi =		{10.4230/TGDK.1.1.9},
  annote =	{Keywords: Trust, Accountability, Autonomy, AI, Knowledge Graphs}
}

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DOI: 10.4230/LIPIcs.ICLP.2012.381

A Logic Programming approach for Access Control over RDF

Authors: Nuno Lopes, Sabrina Kirrane, Antoine Zimmermann, Axel Polleres, and Alessandra Mileo

Published in: LIPIcs, Volume 17, Technical Communications of the 28th International Conference on Logic Programming (ICLP'12) (2012)

Abstract

The Resource Description Framework (RDF) is an interoperable data representation format suitable for interchange and integration of data, especially in Open Data contexts. However, RDF is also becoming increasingly attractive in scenarios involving sensitive data, where data protection is a major concern. At its core, RDF does not support any form of access control and current proposals for extending RDF with access control do not fit well with the RDF representation model. Considering an enterprise scenario, we present a modelling that caters for access control over the stored RDF data in an intuitive and transparent manner. For this paper we rely on Annotated RDF, which introduces concepts from Annotated Logic Programming into RDF. Based on this model of the access control annotation domain, we propose a mechanism to manage permissions via application-specific logic rules. Furthermore, we illustrate how our Annotated Query Language (AnQL) provides a secure way to query this access control annotated RDF data.

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Nuno Lopes, Sabrina Kirrane, Antoine Zimmermann, Axel Polleres, and Alessandra Mileo. A Logic Programming approach for Access Control over RDF. In Technical Communications of the 28th International Conference on Logic Programming (ICLP'12). Leibniz International Proceedings in Informatics (LIPIcs), Volume 17, pp. 381-392, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{lopes_et_al:LIPIcs.ICLP.2012.381,
  author =	{Lopes, Nuno and Kirrane, Sabrina and Zimmermann, Antoine and Polleres, Axel and Mileo, Alessandra},
  title =	{{A Logic Programming approach for Access Control over RDF}},
  booktitle =	{Technical Communications of the 28th International Conference on Logic Programming (ICLP'12)},
  pages =	{381--392},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-43-9},
  ISSN =	{1868-8969},
  year =	{2012},
  volume =	{17},
  editor =	{Dovier, Agostino and Santos Costa, V{\'\i}tor},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2012.381},
  URN =		{urn:nbn:de:0030-drops-36384},
  doi =		{10.4230/LIPIcs.ICLP.2012.381},
  annote =	{Keywords: Logic Programming, Annotation, Access Control, RDF}
}

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Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination

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A Logic Programming approach for Access Control over RDF

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