Abstract
In today’s world, with the shifting nature of artificial intelligence (AI) to explainable AI, which involves humans and machines working and complementing each other, there is a need for a mechanism to govern their collaboration. We have proposed a trust-based mechanism to manage collaboration between them. Our trust-based mechanism has the ability to quantify human trust into a mathematical model. The proposed trust-based framework will facilitate decision making when humans and machines are involved in a process. This framework will ensure that either of them never under trust or over trust each other by computing trust information based on their history. To validate our proposed framework, experiments are performed on Indianapolis Crime Data which contains actual crime information, machine predictions, and police feedback. Results have shown that how the trust of both entities can impact the decision making of the police towards machine predictions.
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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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Kaur, D., Uslu, S., Durresi, A., Mohler, G., Carter, J.G. (2020). Trust-Based Human-Machine Collaboration Mechanism for Predicting Crimes. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_54
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