Secure and trustworthy machine learning/artificial intelligence for multi-domain operations

Danda B. Rawat

doi:10.1117/12.2592860

12 April 2021 Secure and trustworthy machine learning/artificial intelligence for multi-domain operations

Danda B. Rawat

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 1174609 (2021) https://doi.org/10.1117/12.2592860
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Machine Learning (ML) algorithms and Artificial Intelligence (AI) are now regarded as very useful for data-driven applications including resilient multi-domain operations. However, ML algorithms and AI systems can be controlled, dodged, biased, and misled through flawed learning models and input data, they need robust security features and trust. Furthermore, ML algorithms and AI systems add challenges when we have (unlabeled/labeled) sparse/small data or big data for training and evaluation. It is very important to design, evaluate and test ML algorithms and AI systems that produce reliable, robust, trustworthy, explainable, and fair/unbiased outcomes to make them acceptable and reliable in mission critical multi-domain operations. ML algorithms rely on data and work on the principle of ``Garbage In, Garbage Out," which means that if the input data to learning model is corrupted or compromised, the outcomes of the ML/AI would not be optimal, reliable and trustworthy.

Conference Presentation

Citation Download Citation

Danda B. Rawat "Secure and trustworthy machine learning/artificial intelligence for multi-domain operations", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 1174609 (12 April 2021); https://doi.org/10.1117/12.2592860

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