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.

翻译：在交互式机器学习中，特别是在主观或模糊领域，一个重要的挑战是促进人类与模型之间的双向对齐。用户通过数据标注向模型传授其概念定义，同时在此过程中不断精炼自身的理解。为促进这一过程，我们引入了MOCHA——一个基于两种人类概念学习与认知理论构建的交互式机器学习工具。首先，它采用神经符号管道支持基于变异理论的反事实数据生成。通过要求用户标注与已标注数据在句法和语义上相似但被预测为具有不同标签的反例，系统能够更有效地学习，同时帮助用户理解模型并反思其自身的标签定义。其次，MOCHA运用结构对齐理论呈现反例组，帮助用户理解数据项之间可对齐的差异并进行批量标注。我们通过一项包含18名参与者的实验室研究验证了MOCHA的有效性与可用性。