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Machine learning technology has become ubiquitous, but, unfortunately, often exhibits bias. As a consequence, disparate stakeholders need to interact with and make informed decisions about using machine learning models in everyday systems. Visualization technology can support stakeholders in understanding and evaluating trade-offs between, for example, accuracy and fairness of models. This paper aims to empirically answer “Can visualization design choices affect a stakeholder's perception of model bias, trust in a model, and willingness to adopt a model?” Through a series of controlled, crowd-sourced experiments with more than 1,500 participants, we identify a set of strategies people follow in deciding which models to trust. Our results show that men and women prioritize fairness and performance differently and that visual design choices significantly affect that prioritization. For example, women trust fairer models more often than men do, participants value fairness more when it is explained using text than as a bar chart, and being explicitly told a model is biased has a bigger impact than showing past biased performance. We test the generalizability of our results by comparing the effect of multiple textual and visual design choices and offer potential explanations of the cognitive mechanisms behind the difference in fairness perception and trust. Our research guides design considerations to support future work developing visualization systems for machine learning. 

Writing and maintaining UI tests for mobile apps is a time-consuming and tedious task. While decades of research have produced auto- mated approaches for UI test generation, these approaches typically focus on testing for crashes or maximizing code coverage. By contrast, recent research has shown that developers prefer usage-based tests, which center around specific uses of app features, to help support activities such as regression testing. Very few existing techniques support the generation of such tests, as doing so requires automating the difficult task of understanding the semantics of UI screens and user inputs. In this paper, we introduce Avgust, which automates key steps of generating usage-based tests. Avgust uses neural models for image understanding to process video recordings of app uses to synthesize an app-agnostic state-machine encoding of those uses. Then, Avgust uses this encoding to synthesize test cases for a new target app. We evaluate Avgust on 374 videos of common uses of 18 popular apps and show that 69% of the tests Avgust generates successfully execute the desired usage, and that Avgust’s classifiers outperform the state of the art.