License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.FSCD.2023.2
URN: urn:nbn:de:0030-drops-179869
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/17986/
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Jamnik, Mateja

How Can We Make Trustworthy AI? (Invited Talk)

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LIPIcs-FSCD-2023-2.pdf (0.3 MB)

Abstract

Not too long ago most headlines talked about our fear of AI. Today, AI is ubiquitous, and the conversation has moved on from whether we should use AI to how we can trust the AI systems that we use in our daily lives. In this talk I look at some key technical ingredients that help us build confidence and trust in using intelligent technology. I argue that intuitiveness, interaction, explainability and inclusion of human domain knowledge are essential in building this trust. I present some of the techniques and methods we are building for making AI systems that think and interact with humans in more intuitive and personalised ways, enabling humans to better understand the solutions produced by machines, and enabling machines to incorporate human domain knowledge in their reasoning and learning processes.

BibTeX - Entry

@InProceedings{jamnik:LIPIcs.FSCD.2023.2,
  author =	{Jamnik, Mateja},
  title =	{{How Can We Make Trustworthy AI?}},
  booktitle =	{8th International Conference on Formal Structures for Computation and Deduction (FSCD 2023)},
  pages =	{2:1--2:1},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-277-8},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{260},
  editor =	{Gaboardi, Marco and van Raamsdonk, Femke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/17986},
  URN =		{urn:nbn:de:0030-drops-179869},
  doi =		{10.4230/LIPIcs.FSCD.2023.2},
  annote =	{Keywords: AI, human-centric computing, knowledge representation, reasoning, machine learning}
}

Keywords: AI, human-centric computing, knowledge representation, reasoning, machine learning
Collection: 8th International Conference on Formal Structures for Computation and Deduction (FSCD 2023)
Issue Date: 2023
Date of publication: 28.06.2023

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