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Trustworthy Fairness Metric Applied to AI-Based Decisions in Food-Energy-Water

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Advanced Information Networking and Applications (AINA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 450))

Abstract

We propose a Trustworthy Fairness Metric and its measurement methodology to evaluate the fairness of different AI-based solutions proposed by the actors utilizing their trust during decision-making in the Food-Energy-Water (FEW) sectors. Since the standardization of the trustworthiness of AI systems is fundamental, the proposed metric is a compelling advance in this process, whereas other approaches stay at the high-level principles. Trust management system is the basis of the measurement methodology as it incorporates human involvement. This metric captures and quantifies the fairness of the solutions evaluated by the actors having different views and is illustrated in decision-making scenarios generated by AI for FEW sectors. Also, the metric and its measurement methodology can be conveniently adapted to various fields of AI. We present that our metric successfully captures the fairness of solutions in multi-stakeholder decision-making.

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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.

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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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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. University of Technology Sydney, Sydney, NSW, Australia

    Farookh Hussain

  3. Faculty of Bussiness 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. (2022). Trustworthy Fairness Metric Applied to AI-Based Decisions in Food-Energy-Water. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-030-99587-4_37

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