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Elements that Influence Transparency in Artificial Intelligent Systems - A Survey

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Human-Computer Interaction – INTERACT 2023 (INTERACT 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14142))

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Abstract

Artificial Intelligence (AI) models operate as black boxes where most parts of the system are opaque to users. This reduces the user’s trust in the system. Although the Human-Computer Interaction (HCI) community has proposed design practices to improve transparency, work that provides a mapping of these practices and interactive elements that influence AI transparency is still lacking. In this paper, we conduct an in-depth literature survey to identify elements that influence transparency in the field of HCI. Research has shown that transparency allows users to have a better sense of the accuracy, fairness, and privacy of a system. In this context, much research has been conducted on providing explanations for the decisions made by AI systems. Researchers have also studied the development of interactive interfaces that allow user interaction to improve the explanatory capability of systems. This literature review provides key insights about transparency and what the research community thinks about it. Based on the insights gained we gather that a simplified explanation of the AI system is key. We conclude the paper with our proposed idea of representing an AI system, which is an amalgamation of the AI Model (algorithms), data (input and output, including outcomes), and the user interface, as visual interpretations (e.g. Venn diagrams) can aid in understanding AI systems better and potentially making them more transparent.

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Authors and Affiliations

  1. Georgia State University, Atlanta, Ga, 30302, USA

    Deepa Muralidhar & Ashwin Ashok

  2. Univ. Polytechnique Hauts-de-France, LAMIH, CNRS, UMR 8201, 59313, Valenciennes, France

    Rafik Belloum & Kathia Marçal de Oliveira

Authors
  1. Deepa Muralidhar
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  2. Rafik Belloum
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  3. Kathia Marçal de Oliveira
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  4. Ashwin Ashok
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Corresponding author

Correspondence to Deepa Muralidhar .

Editor information

Editors and Affiliations

  1. University of West London, London, UK

    José Abdelnour Nocera

  2. Reykjavik University, Reykjavik, Iceland

    Marta Kristín Lárusdóttir

  3. University of York, York, UK

    Helen Petrie

  4. University of Bari Aldo Moro, Bari, Italy

    Antonio Piccinno

  5. Université Côte d’Azur, Sophia Antipolis Cedex, France

    Marco Winckler

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Muralidhar, D., Belloum, R., de Oliveira, K.M., Ashok, A. (2023). Elements that Influence Transparency in Artificial Intelligent Systems - A Survey. In: Abdelnour Nocera, J., Kristín Lárusdóttir, M., Petrie, H., Piccinno, A., Winckler, M. (eds) Human-Computer Interaction – INTERACT 2023. INTERACT 2023. Lecture Notes in Computer Science, vol 14142. Springer, Cham. https://doi.org/10.1007/978-3-031-42280-5_21

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  • DOI: https://doi.org/10.1007/978-3-031-42280-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42279-9

  • Online ISBN: 978-3-031-42280-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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