Supporting Co-Adaptive Machine Teaching through Human Concept Learning and Cognitive Theories

要旨

An important challenge in interactive machine learning, particularly in subjective or ambiguous domains, is fostering bi-directional alignment between humans and models. Users teach models their concept definition through data labeling, while refining their own understandings throughout the process. To facilitate this, we introduce MOCHA, an interactive machine learning tool informed by two theories of human concept learning and cognition. First, it utilizes a neuro-symbolic pipeline to support Variation Theory-based counterfactual data generation. By asking users to annotate counterexamples that are syntactically and semantically similar to already-annotated data but predicted to have different labels, the system can learn more effectively while helping users understand the model and reflect on their own label definitions. Second, MOCHA uses Structural Alignment Theory to present groups of counterexamples, helping users comprehend alignable differences between data items and annotate them in batch. We validated MOCHA's effectiveness and usability through a lab study with 18 participants.

受賞
Best Paper
著者
Simret Araya. Gebreegziabher
University of Notre Dame, Notre Dame, Indiana, United States
Yukun Yang
University of Notre Dame, Notre Dame, Indiana, United States
Elena L.. Glassman
Harvard University, Allston, Massachusetts, United States
Toby Jia-Jun. Li
University of Notre Dame, Notre Dame, Indiana, United States
DOI

10.1145/3706598.3713708

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713708

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Human-AI Collaboration

G304
7 件の発表
2025-05-01 01:20:00
2025-05-01 02:50:00
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