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Trust and Accountability in Knowledge Graph-Based AI for Self Determination (Dagstuhl Seminar 25051)

Authors: John Domingue, Luis-Daniel Ibáñez, Sabrina Kirrane, Maria-Esther Vidal, and Philipp D. Rohde

Published in: Dagstuhl Reports, Volume 15, Issue 1 (2025)

Abstract

This report documents the program and results of the Dagstuhl Seminar 25051 "Trust and Accountability in Knowledge Graph-Based AI for Self Determination". The seminar focused on AI systems powered by Knowledge Graphs and their fundamental role in powering intelligent decision making. Knowledge Graphs complement Machine Learning algorithms by providing data context and semantics, enabling further inference and question answering capabilities, and their synergy with Large Language Models is being actively researched. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may raise 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 destiny. To counter this threat, AI regulation, such as the EU AI Act, is being proposed in certain regions. 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 fueling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making?

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John Domingue, Luis-Daniel Ibáñez, Sabrina Kirrane, Maria-Esther Vidal, and Philipp D. Rohde. Trust and Accountability in Knowledge Graph-Based AI for Self Determination (Dagstuhl Seminar 25051). In Dagstuhl Reports, Volume 15, Issue 1, pp. 136-200, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@Article{domingue_et_al:DagRep.15.1.136,
  author =	{Domingue, John and Ib\'{a}\~{n}ez, Luis-Daniel and Kirrane, Sabrina and Vidal, Maria-Esther and Rohde, Philipp D.},
  title =	{{Trust and Accountability in Knowledge Graph-Based AI for Self Determination (Dagstuhl Seminar 25051)}},
  pages =	{136--200},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2025},
  volume =	{15},
  number =	{1},
  editor =	{Domingue, John and Ib\'{a}\~{n}ez, Luis-Daniel and Kirrane, Sabrina and Vidal, Maria-Esther and Rohde, Philipp D.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.15.1.136},
  URN =		{urn:nbn:de:0030-drops-236722},
  doi =		{10.4230/DagRep.15.1.136},
  annote =	{Keywords: access control and privacy, federated query processing, intelligent knowledge graph management, programming paradigms for knowledge graphs, semantic data integration}
}

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DOI: 10.4230/TGDK.1.1.9

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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Trust and Accountability in Knowledge Graph-Based AI for Self Determination (Dagstuhl Seminar 25051)

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

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