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Documents authored by Endert, Alex

Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.55
Resiliency: A Consensus Data Binning Method (Short Paper)

Authors: Arpit Narechania, Alex Endert, and Clio Andris

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
Data binning, or data classification, involves grouping quantitative data points into bins (or classes) to represent spatial patterns and show variation in choropleth maps. There are many methods for binning data (e.g., natural breaks, quantile) that may make the same data appear very different on a map. Some of these methods may be more or less appropriate for certain types of data distributions and map purposes. Thus, when designing a map, novice users may be overwhelmed by the number of choices for binning methods and experts may find comparing results from different binning methods challenging. We present resiliency, a new data binning method that assigns areal units to their most agreed-upon, consensus bin as it persists across multiple chosen binning methods. We show how this "smart average" can effectively communicate spatial patterns that are agreed-upon across binning methods. We also measure the variety of bins a single areal unit can be placed in under different binning methods showing fuzziness and uncertainty on a map. We implement resiliency and other binning methods via an open-source JavaScript library, BinGuru.
Cite as

Arpit Narechania, Alex Endert, and Clio Andris. Resiliency: A Consensus Data Binning Method (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 55:1-55:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{narechania_et_al:LIPIcs.GIScience.2023.55,
  author =	{Narechania, Arpit and Endert, Alex and Andris, Clio},
  title =	{{Resiliency: A Consensus Data Binning Method}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{55:1--55:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.55},
  URN =		{urn:nbn:de:0030-drops-189509},
  doi =		{10.4230/LIPIcs.GIScience.2023.55},
  annote =	{Keywords: data binning, data classification, choropleth maps, geovisualization, geographic information systems, geographic information science, cartography}
}
Document
DOI: 10.4230/DagRep.12.8.103
Interactive Visualization for Fostering Trust in ML (Dagstuhl Seminar 22351)

Authors: Polo Chau, Alex Endert, Daniel A. Keim, and Daniela Oelke

Published in: Dagstuhl Reports, Volume 12, Issue 8 (2023)

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

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)

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@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/entities/document/10.4230/DagRep.12.8.103},
  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}
}
Document
DOI: 10.4230/DagRep.10.4.37
Interactive Visualization for Fostering Trust in AI (Dagstuhl Seminar 20382)

Authors: Daniela Oelke, Daniel A. Keim, Polo Chau, and Alex Endert

Published in: Dagstuhl Reports, Volume 10, Issue 4 (2021)

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.
Cite as

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)

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@Article{oelke_et_al:DagRep.10.4.37,
  author =	{Oelke, Daniela and Keim, Daniel A. and Chau, Polo and Endert, Alex},
  title =	{{Interactive Visualization for Fostering Trust in AI (Dagstuhl Seminar 20382)}},
  pages =	{37--42},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2021},
  volume =	{10},
  number =	{4},
  editor =	{Oelke, Daniela and Keim, Daniel A. and Chau, Polo and Endert, Alex},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.10.4.37},
  URN =		{urn:nbn:de:0030-drops-137360},
  doi =		{10.4230/DagRep.10.4.37},
  annote =	{Keywords: accountability, artificial intelligence, explainability, fairness, interactive visualization, machine learning, responsibility, trust, understandability}
}
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