Non-experts increasingly engage in user-driven algorithm auditing, interacting directly with AI systems to probe, document, and reflect on biased behavior. Yet, auditing remains challenging due to model opacity and limited support for navigating and interpreting outputs. This paper explores the design and evaluation of interfaces grounded in the sensemaking framework to support non-experts in auditing gender bias in image captioning. In a between-subjects study, 60 participants audited an image captioning model using one of three interface conditions: a Baseline interface, a Masking Tool for image manipulation, or a Filtering Tool for organizing captions. Our findings show that interface design shaped what participants noticed, how they interpreted model behavior, and supported their hypotheses. The Image Masking Tool enabled fine-grained testing of visual cues and context, while the Text Filtering Tool revealed broader asymmetries in gendered language. We argue that incorporating sensemaking into auditing practices can advance accountability and transparency in machine learning systems.

While human-centered approaches to machine learning explore various human roles within the interaction loop, the notion of Interactive Machine Teaching (IMT) emerged with a focus on leveraging the teaching skills of humans as a teacher to build machine learning systems. However, most systems and studies are devoted to single users. In this article, we study collaborative interactive machine teaching in the context of image classification to analyze how people can structure the teaching process collectively and to understand their experience. Our contributions are threefold. First, we developed a web application called TeachTOK that enables groups of users to curate data and train a model together incrementally. Second, we conducted a study in which ten participants were divided into three teams that competed to build an image classifier in nine days. Qualitative results of participants’ discussions in focus groups reveal the emergence of collaboration patterns in the machine teaching task, how collaboration helps revise teaching strategies and participants’ reflections on their interaction with the TeachTOK application. From these findings we provide implications for the design of more interactive, collaborative and participatory machine learning-based systems.