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• W4366729322 abstract "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." @default.
• W4366729322 created "2023-04-24" @default.
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• W4366729322 date "2023-04-19" @default.
• W4366729322 modified "2023-10-18" @default.
• W4366729322 title "Kaleidoscope: Semantically-grounded, context-specific ML model evaluation" @default.
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• W4366729322 doi "https://doi.org/10.1145/3544548.3581482" @default.
• W4366729322 hasPublicationYear "2023" @default.
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