To exhibit machine intelligence, it is critical for the system to not only make intelligent decisions, but also be able to explain how it arrives at those decisions. Explainable machine learning is an emerging research area aiming to develop new or modified machine learning techniques that will produce more explainable models. This project will develop novel methods to interpret the predictions of a class of learning models known as "pairwise learning models," which predicts relationships between instances rather than specific properties of an individual instance. For example, a consumer might want to know why the system recommends product A as similar to product B. The results of this research may benefit many real-world applications that are involved in pairwise learning, such as face recognition, visual tracking, information retrieval and bioinformatics. The development of the proposed approaches will contribute to the exploration of explainable machine learning as well as machine learning in general.
Two exploratory research tasks are carried out to generate explanations on both individual predictions (local interpretations) and the entire model behaviors (global interpretation). The proposed local interpretation method adapts the concept of Shapley-value to explain prediction decisions about an arbitrary pair of input instances. The proposed global explanation method is a Bayesian non-parametric pairwise interpretation method with the elastic nets, which will explain feature importance across a population. Such global interpretability can facilitate the understanding of the sensitivity levels of a target pairwise learning model to specific input dimensions.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
