Abstract
Artificial Intelligence is playing pivotal role in automation of processes that were considered hard problems previously, but trustworthiness of these systems is still under question as many of these systems fail to meet expectations. Trustworthiness of artificial intelligence based systems depend on many factors. This paper analyzes human trust lifecycle and proposes an end to end agile and automated machine learning framework for automation of development, deployment, monitoring, and enhancements of AI/ML processes. Further this paper presents results of initial deployments of proposed framework and compares them with benchmark results.
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Manchanda, S. (2023). End to End Agile and Automated Machine Learning Framework for Trustworthy, Reliable and Sustainable Artificial Intelligence. In: Goswami, S., Barara, I.S., Goje, A., Mohan, C., Bruckstein, A.M. (eds) Data Management, Analytics and Innovation. ICDMAI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 137. Springer, Singapore. https://doi.org/10.1007/978-981-19-2600-6_3
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DOI: https://doi.org/10.1007/978-981-19-2600-6_3
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