Secure and Trustworthy Machine Learning and Artificial Intelligence for Multi-Domain Applications

Speaker:  Danda B Rawat – Washington, DC, United States
Topic(s):  Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing


Machine Learning (ML) algorithms and Artificial Intelligence (AI) systems have already had an immense impact on our society and have been shown to be able to create machine cognition comparable to or even better than human cognition for some applications. Machine learning algorithms are now regarded as very useful for data-driven applications including cybersecurity solutions for different emerging applications. However, because 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. 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 by diverse users for different applications. This talk will focus on both AI for cybersecurity and cybersecurity for AI for multi-domain operations and applications. This talk will also cover emerging applications and use cases of secure and trustworthy AI/ML and their success and pitfalls.

About this Lecture

Number of Slides:  30 - 45
Duration:  45 - 60 minutes
Languages Available:  English
Last Updated: 

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