2 Search Results for "Vidal, Maria-Esther"

Document
Vision
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.
Cite as

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)

Copy BibTex To Clipboard

@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-dev.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}
}
Document
DOI: 10.4230/DagRep.7.6.135
Federated Semantic Data Management (Dagstuhl Seminar 17262)

Authors: Olaf Hartig, Maria-Esther Vidal, and Johann-Christoph Freytag

Published in: Dagstuhl Reports, Volume 7, Issue 6 (2018)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 17262 "Federated Semantic Data Management" (FSDM). The purpose of the seminar was to gather experts from the Semantic Web and Database communities, together with experts from application areas, to discuss in-depth open issues that have impeded FSDM approaches to be used on a large scale. The discussions were centered around the following four themes, each of which was the focus of a separate working group: i) graph data models, ii) federated query processing, iii) access control and privacy, and iv) use cases and applications. The main outcome of the seminar is a deeper understanding of the state of the art and of the open challenges of FSDM.
Cite as

Olaf Hartig, Maria-Esther Vidal, and Johann-Christoph Freytag. Federated Semantic Data Management (Dagstuhl Seminar 17262). In Dagstuhl Reports, Volume 7, Issue 6, pp. 135-167, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

Copy BibTex To Clipboard

@Article{hartig_et_al:DagRep.7.6.135,
  author =	{Hartig, Olaf and Vidal, Maria-Esther and Freytag, Johann-Christoph},
  title =	{{Federated Semantic Data Management (Dagstuhl Seminar 17262)}},
  pages =	{135--167},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2017},
  volume =	{7},
  number =	{6},
  editor =	{Hartig, Olaf and Vidal, Maria-Esther and Freytag, Johann-Christoph},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.7.6.135},
  URN =		{urn:nbn:de:0030-drops-82890},
  doi =		{10.4230/DagRep.7.6.135},
  annote =	{Keywords: Linked Data, Query Processing, RDF, SPARQL}
}
  • Refine by Author
  • 2 Vidal, Maria-Esther
  • 1 Domingue, John
  • 1 Freytag, Johann-Christoph
  • 1 Hartig, Olaf
  • 1 Ibáñez, Luis-Daniel
  • Show More...

  • Refine by Classification
  • 1 Computing methodologies → Distributed artificial intelligence
  • 1 Computing methodologies → Knowledge representation and reasoning
  • 1 Human-centered computing → Collaborative and social computing theory, concepts and paradigms
  • 1 Security and privacy → Human and societal aspects of security and privacy
  • 1 Social and professional topics → Computing / technology policy

  • Refine by Keyword
  • 1 AI
  • 1 Accountability
  • 1 Autonomy
  • 1 Knowledge Graphs
  • 1 Linked Data
  • Show More...

  • Refine by Type
  • 2 document

  • Refine by Publication Year
  • 1 2017
  • 1 2023

Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact