Kaleidoscope: Semantically-grounded, Context-specific ML Model Evaluation

Harini Suresh MIT CSAIL

Divya Shanmugam MIT CSAIL

Tiffany Chen MIT CSAIL

Annie Bryan MIT CSAIL

Alexander D'Amour Google Research

John V. Guttag MIT CSAIL

Arvind Satyanarayan MIT CSAIL

ACM Human Factors in Computing Systems (CHI), 2023

Kaleidoscope’s workflow consists of identifying meaningful examples, generalizing them into larger, diverse sets representing important concepts, and using these concepts to specify and test model behavior.


Desired model behavior often differs across contexts (e.g., different geographies, communities, or institutions), but there is little infrastructure to facilitate context-specific evaluations key to deployment decisions and building trust. Here, we present Kaleidoscope, a system for evaluating models in terms of user-driven, domain-relevant concepts. Kaleidoscope’s iterative workflow enables generalizing from a few examples into a larger, diverse set representing an important concept. These example sets can be used to test model outputs or shifts in model behavior in semantically-meaningful ways. For instance, we might construct a “xenophobic comments” set and test that its examples are more likely to be flagged by a content moderation model than a “civil discussion” set. To evaluate Kaleidoscope, we compare it against template- and DSL-based grouping methods, and conduct a usability study with 13 Reddit users testing a content moderation model. We find that Kaleidoscope facilitates iterative, exploratory hypothesis testing across diverse, conceptually-meaningful example sets.