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Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning

It is indicated that data scientists over-trust and misuse interpretability tools, and few of their participants were able to accurately describe the visualizations output by these tools.
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Putting Users in Control of their Recommendations

This work builds and evaluates a system that incorporates user-tuned popularity and recency modifiers, allowing users to express concepts like "show more popular items" and finds that users who are given these controls evaluate the resulting recommendations much more positively.
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CrowdMask: Using Crowds to Preserve Privacy in Crowd-Powered Systems via Progressive Filtering

This work presents a crowd-powered approach to masking private content in data by segmenting and distributing smaller segments to crowd workers so that individual workers can identify potentially private content without being able to fully view it themselves.
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Optimizing for Happiness and Productivity: Modeling Opportune Moments for Transitions and Breaks at Work

This study builds models to predict whether a person should continue their task, transition to a new task, or take a break from a three-week field study, and asks users to evaluate the timing of recommendations provided by a recommender that relies on these models.
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Mercury: Empowering Programmers' Mobile Work Practices with Microproductivity

Mercury is introduced, a system that guides programmers in making progress on-the-go with auto-generated microtasks derived from their source code's current state that suggest an opportunity exists to support the continuation of programming tasks across devices and help programmers resume coding upon returning to their workspace.
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Supporting Workplace Detachment and Reattachment with Conversational Intelligence

It is found that workers send fewer e-mails after work hours and spend a larger percentage of their first hour at work using productivity applications than they normally would when using SwitchBot, a conversational bot which engages with workers at the start and end of their work day.
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From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence

This work surveys the literature on human explanation in philosophy, cognitive science, and the social sciences, and proposes a list of design principles for machine-generated explanations that are meaningful to humans, and develops a method for generating explanations that adhere to these principles.
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Perception of Students regarding online classes - adapting the New Normal

Abstract Background The COVID-19 pandemic has halted teaching in variety of institutions, especially in dental schools. Educational directors had to refined education from the formerly class based…
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Towards More Robust Speech Interactions for Deaf and Hard of Hearing Users

A better understanding of the challenges of deaf speech recognition is contributed and insights for future system development are provided, including the potential for groups to collectively exceed the performance of individuals.
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“I Didn’t Know I Looked Angry”: Characterizing Observed Emotion and Reported Affect at Work

This paper compares the continuous observed emotion output from an AER tool to discrete reported affect obtained via a one-day combined tool-use and diary study, and provides empirical evidence showing that these signals do not completely align.
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