Shared Interest: Measuring Human-ai Alignment To Identify Recurring Patterns In Model Behavior
Angie Boggust, Benjamin Hoover, Arvind Satyanarayan, Hendrik Strobelt . CHI Conference on Human Factors in Computing Systems 2022 – 43 citations
[Paper]
Saliency methods – techniques to identify the importance of input features on a model’s output – are a common step in understanding neural network behavior. However, interpreting saliency requires tedious manual inspection to identify and aggregate patterns in model behavior, resulting in ad hoc or cherry-picked analysis. To address these concerns, we present Shared Interest: metrics for comparing model reasoning (via saliency) to human reasoning (via ground truth annotations). By providing quantitative descriptors, Shared Interest enables ranking, sorting, and aggregating inputs, thereby facilitating large-scale systematic analysis of model behavior. We use Shared Interest to identify eight recurring patterns in model behavior, such as cases where contextual features or a subset of ground truth features are most important to the model. Working with representative real-world users, we show how Shared Interest can be used to decide if a model is trustworthy, uncover issues missed in manual analyses, and enable interactive probing.