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Interactive Visualization for Fostering Trust in AI (Dagstuhl Seminar 20382)

Authors Daniela Oelke, Daniel A. Keim, Polo Chau, Alex Endert and all authors of the abstracts in this report

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DagRep.10.4.37.pdf
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ACM Subject Classification
  • Computing methodologies → Artificial intelligence
  • Human-centered computing → Visualization
  • Computing methodologies → Machine learning
Keywords
  • accountability
  • artificial intelligence
  • explainability
  • fairness
  • interactive visualization
  • machine learning
  • responsibility
  • trust
  • understandability

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Abstract

Artificial intelligence (AI), and in particular machine learning algorithms, are of increasing importance in many application areas but interpretability and understandability as well as responsibility, accountability, and fairness of the algorithms' results, all crucial for increasing the humans' trust into the systems, are still largely missing. Big industrial players, including Google, Microsoft, and Apple, have become aware of this gap and recently published their own guidelines for the use of AI in order to promote fairness, trust, interpretability, and other goals. Interactive visualization is one of the technologies that may help to increase trust in AI systems. During the seminar, we discussed the requirements for trustworthy AI systems as well as the technological possibilities provided by interactive visualizations to increase human trust in AI.

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Daniela Oelke, Daniel A. Keim, Polo Chau, and Alex Endert. Interactive Visualization for Fostering Trust in AI (Dagstuhl Seminar 20382). In Dagstuhl Reports, Volume 10, Issue 4, pp. 37-42, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/DagRep.10.4.37

Author Details

Daniela Oelke
  • Hochschule Offenburg, DE
Daniel A. Keim
  • Universität Konstanz, DE
Polo Chau
  • Georgia Tech, US
Alex Endert
  • Georgia Tech, US
and all authors of the abstracts in this report
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