Keynote: Towards Explainability in AI and Multimedia Research

Abstract

AI as a concept has been around since the 1950's. With the recent advancements in machine learning technology, and the availability of big data and large computing processing power, the scene is set for AI to be used in many more systems and applications which will profoundly impact society. The current deep learning based AI systems are mostly in black box form and are often non-explainable. Though it has high performance, it is also known to make occasional fatal mistakes. This has limited the applications of AI, especially in mission critical problems such as decision support, command and control, and other life-critical operations. This talk focuses on explainable AI, which holds promise in helping humans to better understand and interpret the decisions made by black-box AI models. Current research efforts towards explainable multimedia AI center on two parts of solution. The first part focuses on better understanding of multimedia content, especially video. This includes dense annotation of video content from not just object recognition, but also relation inference. The relation includes both correlation and causality relations, as well as common sense knowledge. The dense annotation enables us to transform the level of representation of video towards that of language, in the form of relation triplets and relation graphs, and permits in-depth research on flexible descriptions, question-answering and knowledge inference of video content. A large scale video dataset has been created to support this line of research. The second direction focuses on the development of explainable AI models, which are just beginning. Existing works focus on either the intrinsic approach, which designs self-explanatory models, or post-hoc approach, which constructs a second model to interpret the target model. Both approaches have limitations on trade-offs between interpretability and accuracy, and the lack of guarantees about the explanation quality. In addition, there are issues of quality, fairness, robustness and privacy in model interpretation. In this talk, I present current state-of-the arts approaches in explainable multimedia AI, along with our preliminary research on relation inference in videos, as well as leveraging prior domain knowledge, information theoretic principles, and adversarial algorithms to achieving interpretability. I will also discuss future research towards quality, fairness and robustness of interpretable AI.