https://www.dagstuhl.de/20382

September 13 – 16 , 2020, Dagstuhl Seminar 20382

Interactive Visualization for Fostering Trust in AI

Organizers

Polo Chau (Georgia Institute of Technology – Atlanta, US)
Alex Endert (Georgia Institute of Technology – Atlanta, US)
Daniel A. Keim (Universität Konstanz, DE)
Daniela Oelke (Siemens AG – München, DE)

For support, please contact

Susanne Bach-Bernhard for administrative matters

Shida Kunz for scientific matters

Documents

Dagstuhl Seminar Schedule (Upload here)

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Motivation

Artificial intelligence, and in particular machine learning algorithms, are of increasing importance in many application areas. However, interpretability, understandability, responsibility, accountability, and fairness of the algorithms' results - all crucial for increasing humans' trust into the systems - are still largely missing. All major industrial players, including Google, Microsoft, Apple, and SAP, have become aware of this gap and recently published some form of Guidelines for the Use of AI. Interactive visualization is one of the technologies that has strong potential to increase trust into AI systems.

In our seminar, we want to discuss the requirements for trustworthy AI systems as well as the technological possibilities provided by interactive visualizations to increase human trust in AI. As a first step, we will identify the factors that help to increase the trust of users in AI systems. This involves a discussion of their understandability (interpretable, explainable, intelligible, etc.) as well as their responsibility (accountable, transparent, fair, unbiased, etc.) since these factors drive the design and development of interactive interfaces and AI models to ensure trust. Next, the role that visualizations play in increasing trust in AI system will be illuminated. This includes questions such as: Which mechanisms exist to make AI systems trustworthy? How can interactive visualizations contribute? Under which circumstances are interactive visualizations the decisive factor for enabling responsible AI? And what are the research challenges that still have to be solved – in the area of machine learning or interactive visualization – to leverage this potential in real world applications?

The planned outcome of this seminar is a better understanding of how interactive visualizations can help to foster trust in artificial intelligence systems by making them more understandable and responsible. This should encourage innovative research and help to start joint research projects tackling the issue. Concrete outcomes may be a position paper describing the research challenges identified in the seminar or a special issue featuring interactive visualizations for fostering trust in AI.

Motivation text license
  Creative Commons BY 3.0 DE
  Polo Chau, Alex Endert, Daniel A. Keim, and Daniela Oelke

Classification

  • Graphics
  • Human-Computer Interaction
  • Machine Learning

Keywords

  • Interactive visualization
  • Machine learning
  • Trust
  • Responsibility
  • Understandability

Documentation

In the series Dagstuhl Reports each Dagstuhl Seminar and Dagstuhl Perspectives Workshop is documented. The seminar organizers, in cooperation with the collector, prepare a report that includes contributions from the participants' talks together with a summary of the seminar.

 

Download overview leaflet (PDF).

Publications

Furthermore, a comprehensive peer-reviewed collection of research papers can be published in the series Dagstuhl Follow-Ups.

Dagstuhl's Impact

Please inform us when a publication was published as a result from your seminar. These publications are listed in the category Dagstuhl's Impact and are presented on a special shelf on the ground floor of the library.