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Interactive Visualization for Fostering Trust in ML (Dagstuhl Seminar 22351)

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

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DagRep.12.8.103.pdf
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Subject Classification

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

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Abstract

The use of artificial intelligence continues to impact a broad variety of domains, application areas, and people. However, interpretability, understandability, responsibility, accountability, and fairness of the algorithms' results - all crucial for increasing humans' trust into the systems - are still largely missing. The purpose of this seminar is to understand how these components factor into the holistic view of trust. Further, this seminar seeks to identify design guidelines and best practices for how to build interactive visualization systems to calibrate trust.

Cite As Get BibTex

Polo Chau, Alex Endert, Daniel A. Keim, and Daniela Oelke. Interactive Visualization for Fostering Trust in ML (Dagstuhl Seminar 22351). In Dagstuhl Reports, Volume 12, Issue 8, pp. 103-116, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/DagRep.12.8.103

Author Details

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