License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.12.8.103
URN: urn:nbn:de:0030-drops-177161
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/17716/
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Chau, Polo ; Endert, Alex ; Keim, Daniel A. ; Oelke, Daniela
Weitere Beteiligte (Hrsg. etc.): Polo Chau and Alex Endert and Daniel A. Keim and Daniela Oelke

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

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dagrep_v012_i008_p103_22351.pdf (2 MB)

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.

BibTeX - Entry

@Article{chau_et_al:DagRep.12.8.103,
  author =	{Chau, Polo and Endert, Alex and Keim, Daniel A. and Oelke, Daniela},
  title =	{{Interactive Visualization for Fostering Trust in ML (Dagstuhl Seminar 22351)}},
  pages =	{103--116},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2023},
  volume =	{12},
  number =	{8},
  editor =	{Chau, Polo and Endert, Alex and Keim, Daniel A. and Oelke, Daniela},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/17716},
  URN =		{urn:nbn:de:0030-drops-177161},
  doi =		{10.4230/DagRep.12.8.103},
  annote =	{Keywords: accountability, artificial intelligence, explainability, fairness, interactive visualization, machine learning, responsibility, trust, understandability}
}

Keywords: accountability, artificial intelligence, explainability, fairness, interactive visualization, machine learning, responsibility, trust, understandability
Collection: DagRep, Volume 12, Issue 8
Issue Date: 2023
Date of publication: 02.03.2023

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