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Trustworthy AI: Deciding What to Decide

A Strategic Decision on Credit Default Swaps Investment

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Intelligent Computing (SAI 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1016))

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Abstract

When engaging in strategic decision-making, we are frequently confronted with overwhelming information and data. The situation can be further complicated when certain pieces of evidence contradict each other or become paradoxical. The primary challenge is how to determine which information can be trusted when we adopt Artificial Intelligence (AI) systems for decision-making. This issue is known as “deciding what to decide” or Trustworthy AI. However, the AI system itself is often considered an opaque “black box”. We propose a new approach to address this issue by introducing a novel framework of Trustworthy AI (TAI) encompassing three crucial components of AI: representation space, loss function, and optimizer. Each component is loosely coupled with four TAI properties. Altogether, the framework consists of twelve TAI properties. We aim to use this framework to conduct the TAI experiments by quantitive and qualitative research methods to satisfy TAI properties for the decision-making context. The framework allows us to formulate an optimal prediction model trained by the given dataset for applying the strategic investment decision of credit default swaps (CDS) in the technology sector. Finally, we provide our view of the future direction of TAI research.

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Acknowledgment

This research was funded by the Luxembourg National Research Fund (FNR), grant ID C21/IS/16221483/CBD and grant ID 15748747. For open access, the author has applied a Creative Commons Attribution 4.0 International (CC BY 4.0) license to any Author Accepted Manuscript version arising from this submission.

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Authors and Affiliations

  1. University of Luxembourg, 6 Avenue de la Fonte, l-4364, Esch-sur-Alzette, Luxembourg

    Caesar Wu, Jingjing Xu & Pascal Bouvry

  2. Monash University, 20 Exhibition Walk, Clayton, VIC, 3800, Australia

    Yuan-Fang Li

  3. Dongbei University of Finance and Economics, Dalian, 116025, China

    Jian Li

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  1. Caesar Wu
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  2. Yuan-Fang Li
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  3. Jian Li
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  4. Jingjing Xu
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  5. Pascal Bouvry
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Correspondence to Caesar Wu .

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  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Wu, C., Li, YF., Li, J., Xu, J., Bouvry, P. (2024). Trustworthy AI: Deciding What to Decide. In: Arai, K. (eds) Intelligent Computing. SAI 2024. Lecture Notes in Networks and Systems, vol 1016. Springer, Cham. https://doi.org/10.1007/978-3-031-62281-6_8

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