Secure and Trustworthy Machine Learning/Artificial Intelligence for Multi-Domain Operations

Machine Learning (ML) algorithms and Artificial Intelligence (AI) systems have already had an immense impact on our society as they have shown to be able to create machine cognition comparable to or even better than human cognition for some applications. ML algorithms 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. This paper focuses on achieving secure and trustworthy machine learning and artificial intelligence operations using context aware selection of learning models and blockchain for multi-domain battlefield operations.