The wide adoption of Machine Learning (ML) technologies has created a growing demand for people who can train ML models. Some advocated the term "machine teacher'' to refer to the role of people who inject domain knowledge into ML models. This "teaching'' perspective emphasizes supporting the productivity and mental wellbeing of machine teachers through efficient learning algorithms and thoughtful design of human-AI interfaces. One promising learning paradigm is Active Learning (AL), by which the model intelligently selects instances to query a machine teacher for labels, so that the labeling workload could be largely reduced. However, in current AL settings, the human-AI interface remains minimal and opaque. A dearth of empirical studies further hinders us from developing teacher-friendly interfaces for AL algorithms. In this work, we begin considering AI explanations as a core element of the human-AI interface for teaching machines. When a human student learns, it is a common pattern to present one's own reasoning and solicit feedback from the teacher. When a ML model learns and still makes mistakes, the teacher ought to be able to understand the reasoning underlying its mistakes. When the model matures, the teacher should be able to recognize its progress in order to trust and feel confident about their teaching outcome. Toward this vision, we propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the surging field of explainable AI (XAI) into an AL setting. We conducted an empirical study comparing the model learning outcomes, feedback content and experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without explanation). Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and additional cognitive workload. Our study also reveals important individual factors that mediate a machine teacher's reception to AI explanations, including task knowledge, AI experience and Need for Cognition. By reflecting on the results, we suggest future directions and design implications for XAL, and more broadly, machine teaching through AI explanations.

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Figure 1: Overview of DramatVis Personae (DVP). (a) Rich text editor. (b) Characters and Demographics panel for listing characters identifed by the system, merging aliases, and assigning social identities to characters. (c) Timeline representation of the story (Peter Pan by J. M. Barrie (1911)) showing every mentions of characters as well as the total number of mentions of characters. (d) Word zone [38] showing sample adjectives used for the selected characters (Peter Pan and Wendy).

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Stuttering is a speech disorder which impacts the personal and professional lives of millions of people worldwide. To save themselves from stigma and discrimination, people who stutter (PWS) may adopt different strategies to conceal their stuttering. One of the common strategies is word substitution where an individual avoids saying a word they might stutter on and use an alternative instead. This process itself can cause stress and add more burden. In this work, we present Fluent, an AI augmented writing tool which assists PWS in writing scripts which they can speak more fluently. Fluent embodies a novel active learning based method of identifying words an individual might struggle pronouncing. Such words are highlighted in the interface. On hovering over any such word, Fluent presents a set of alternative words which have similar meaning but are easier to speak. The user is free to accept or ignore these suggestions. Based on such user interaction (feedback), Fluent continuously evolves its classifier to better suit the personalized needs of each user. We evaluated our tool by measuring its ability to identify difficult words for 10 simulated users. We found that our tool can identify difficult words with a mean accuracy of over 80% in under 20 interactions and it keeps improving with more feedback. Our tool can be beneficial for certain important life situations like giving a talk, presentation, etc. The source code for this tool has been made publicly accessible at github.com/bhavyaghai/Fluent. CCS CONCEPTS• Social and professional topics → People with disabilities; • Human-centered computing → Interactive systems and tools;• Computing methodologies → Active learning settings.