Shared Interest: Large-Scale Visual Analysis of Model Behavior by Measuring Human-AI Alignment

• URL: http://arxiv.org/abs/2107.09234v1
• Date: Tue, 20 Jul 2021 02:44:39 GMT
• Title: Shared Interest: Large-Scale Visual Analysis of Model Behavior by Measuring Human-AI Alignment
• Authors: Angie Boggust, Benjamin Hoover, Arvind Satyanarayan, Hendrik Strobelt
• Abstract summary: Saliency is a technique to identify the importance of input features on a model's output. We present Shared Interest: a set of metrics for comparing saliency with human annotated ground truths. We show how Shared Interest can be used to rapidly develop or lose trust in a model's reliability.
• Score: 15.993648423884466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Saliency methods -- techniques to identify the importance of input features on a model's output -- are a common first 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: a set of metrics for comparing saliency with human annotated ground truths. By providing quantitative descriptors, Shared Interest allows ranking, sorting, and aggregation of inputs thereby facilitating large-scale systematic analysis of model behavior. We use Shared Interest to identify eight recurring patterns in model behavior including focusing on a sufficient subset of ground truth features or being distracted by contextual features. Working with representative real-world users, we show how Shared Interest can be used to rapidly develop or lose trust in a model's reliability, uncover issues that are missed in manual analyses, and enable interactive probing of model behavior.

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