{'fraction_non_alphanumeric': 0.02564102564102564, 'fraction_numerical': 0.0, 'mean_word_length': 5.434065934065934, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Shared Interest: Large-Scale Visual Analysis of Model Behavior by Measuring Human-AI Alignment

Saliency methods — techniques to identify the im-portance 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 quantita-tive descriptors, Shared Interest allows ranking, sorting, and aggregation of inputs thereby facil-itating 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.