Trusted and Responsible AI (Explainability, Adversarial, Bias, Fairness) - Dr. Vamsi Mohan Vandrangi, Huber
Artificial intelligence is growing into a powerful technology. With enormous power of AI, comes great responsibility. AI raises worries on a number of fronts due to its potential for disruption. There are many concerns like workforce displacement, privacy, potential biases in decision making. Explainable artificial intelligence (XAI) is a set of processes and strategies that allow humans to understand and trust ML algorithms'. XAI is one of the important prerequisites for adopting responsible AI, a methodology for deploying AI approaches in real businesses on a big scale while maintaining justice, explainability, and responsibility. As AI becomes widely used, "adversarial AI" emerges, as attackers take advantage of ML algorithms. Many businesses focus their security expenditures on hardware and software attack surfaces, leaving a critical vulnerability unprotected. "Bias in AI" has been a topic of research and concern in ML area, and it has gained in popularity among broad consumers in recent years as AI understanding has increased. Dr. Vamsi Mohan Vandrangi will be talking on the Trusted and Responsible AI. He will discuss on the Explainability (XAI), Adversarial AI/ML and possible attacking methods, Bias in AI and their fixing methods, and Fairness in AI systems.
