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Trustworthy Acceptance: A New Metric for Trustworthy Artificial Intelligence Used in Decision Making in Food–Energy–Water Sectors

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Advanced Information Networking and Applications (AINA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 225))

Abstract

We propose, for the first time, a trustworthy acceptance metric and its measurement methodology to evaluate the trustworthiness of AI-based systems used in decision making in Food Energy Water (FEW) management. The proposed metric is a significant step forward in the standardization process of AI systems. It is essential to standardize the AI systems’ trustworthiness, but until now, the standardization efforts remain at the level of high-level principles. The measurement methodology of the proposed includes human experts in the loop, and it is based on our trust management system. Our metric captures and quantifies the system’s transparent evaluation by field experts on as many control points as desirable by the users. We illustrate the trustworthy acceptance metric and its measurement methodology using AI in decision-making scenarios of Food-Energy-Water sectors. However, the proposed metric and its methodology can be easily adapted to other fields of AI applications. We show that our metric successfully captures the aggregated acceptance of any number of experts, can be used to do multiple measurements on various points of the system, and provides confidence values for the measured acceptance.

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Acknowledgements

This work was partially supported by the National Science Foundation under Grant No.1547411 and by the U.S. Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) (Award Number 2017-67003-26057) via an interagency partnership between USDA-NIFA and the National Science Foundation (NSF) on the research program Innovations at the Nexus of Food, Energy and Water Systems.

Author information

Authors and Affiliations

  1. Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA

    Suleyman Uslu, Davinder Kaur & Arjan Durresi

  2. Oregon State University, Corvallis, OR, USA

    Samuel J. Rivera & Meghna Babbar-Sebens

  3. European University of Tirana, Tirana, Albania

    Mimoza Durresi

Authors
  1. Suleyman Uslu
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  2. Davinder Kaur
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  3. Samuel J. Rivera
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  4. Arjan Durresi
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  5. Mimoza Durresi
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  6. Meghna Babbar-Sebens
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Corresponding author

Correspondence to Arjan Durresi .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, ON, Canada

    Isaac Woungang

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Uslu, S., Kaur, D., Rivera, S.J., Durresi, A., Durresi, M., Babbar-Sebens, M. (2021). Trustworthy Acceptance: A New Metric for Trustworthy Artificial Intelligence Used in Decision Making in Food–Energy–Water Sectors. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-030-75100-5_19

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