Explainable AI (XAI) has established itself as an important component of AI-driven interactive systems. With Augmented Reality (AR) becoming more integrated in daily lives, the role of XAI also becomes essential in AR because end-users will frequently interact with intelligent services. However, it is unclear how to design effective XAI experiences for AR. We propose XAIR, a design framework that addresses when, what, and how to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users’ preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR. XAIR’s utility and effectiveness was verified via a study with 10 designers and another study with 12 end-users. XAIR can provide guidelines for designers, inspiring them to identify new design opportunities and achieve effective XAI designs in AR.

We present a method for identifying groups of test examples---slices---on which a model under-performs, a task now known as slice discovery. We formalize coherence---a requirement that erroneous predictions, within a slice, should be wrong for the same reason---as a key property that any slice discovery method should satisfy. We then use influence functions to derive a new slice discovery method, InfEmbed, which satisfies coherence by returning slices whose examples are influenced similarly by the training data. InfEmbed is simple, and consists of applying K-Means clustering to a novel representation we deem influence embeddings. We show InfEmbed outperforms current state-of-the-art methods on 2 benchmarks, and is effective for model debugging across several case studies.

In the Multiple Instance Learning (MIL) scenario, the training data consists of instances grouped into bags. Bag labels indicate whether each bag contains at least one positive instance, but instance labels are not observed. Recently, Haussmann et al (CVPR 2017) tackled the MIL instance label prediction task by introducing the Multiple Instance Learning Gaussian Process Logistic (MIL-GP-Logistic) model, an adaptation of the Gaussian Process Logistic Classification model that inherits its uncertainty quantification and flexibility. Notably, they provide a fast mean-field variational inference procedure. However, due to their choice of the logistic link, they do not maximize the ELBO objective directly, but rather a lower bound on it. This approximation, as we show, hurts predictive performance. In this work, we propose the Multiple Instance Learning Gaussian Process Probit (MIL-GP-Probit) model, an adaptation of the Gaussian Process Probit Classification model to solve the MIL instance label prediction problem. Leveraging the analytical tractability of the probit link, we give a variational inference procedure based on variable augmentation that maximizes the ELBO objective directly. Applying it, we show MIL-GP-Probit is significantly more calibrated than MIL-GP-Logistic on all 20 datasets of the benchmark 20 Newsgroups dataset collection, and achieves higher AUC than MIL-GP-Logistic on an additional 51 out of 59 datasets. Furthermore, we show how the probit formulation enables principled bag label predictions and a Gibbs sampling scheme. This is the first exact posterior inference procedure for any Bayesian model for the MIL scenario.

Falling rule lists are classification models consisting of an ordered list of if-then rules, where (i) the order of rules determines which example should be classified by each rule, and (ii) the estimated probability of success decreases monotonically down the list. These kinds of rule lists are inspired by healthcare applications where patients would be stratified into risk sets and the highest at-risk patients should be considered first. We provide a Bayesian framework for learning falling rule lists that does not rely on traditional greedy decision tree learning methods.