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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 , Maria-Esther Vidal



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Luis-Daniel Ibáñez
  • Department of Electronics and Computer Science, University of Southampton, UK
John Domingue
  • Knowledge Media Institute, The Open University, Milton Keynes, UK
Sabrina Kirrane
  • Institute for Information Systems & New Media, Vienna University of Economics and Business, Austria
Oshani Seneviratne
  • Department of Computer Science, Rensselaer Polytechnic Institute, USA
Aisling Third
  • Knowledge Media Institute, The Open University, Milton Keynes, UK
Maria-Esther Vidal
  • Leibniz University of Hannover, Germany
  • TIB-Leibniz Information Centre of Science and Technology, Hannover, Germany
  • L3S Research Centre, Hannover, Germany

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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)
https://doi.org/10.4230/TGDK.1.1.9

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.

Subject Classification

ACM Subject Classification
  • Social and professional topics → Computing / technology policy
  • Computing methodologies → Knowledge representation and reasoning
  • Human-centered computing → Collaborative and social computing theory, concepts and paradigms
  • Security and privacy → Human and societal aspects of security and privacy
  • Computing methodologies → Distributed artificial intelligence
Keywords
  • Trust
  • Accountability
  • Autonomy
  • AI
  • Knowledge Graphs

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