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Despite the evidence of harm that technology can inflict, commensurate policymaking to hold tech platforms accountable still lags. This is pertinent to app-based gig workers, where unregulated algorithms continue to dictate their work, often with little human recourse. While past HCI literature has investigated workers’ experiences under algorithmic management and how to design interventions, rarely are the perspectives of stakeholders who inform or craft policy sought. To bridge this, we propose using data probes---interactive visualizations of workers’ data that show the impact of technology practices on people---exploring them in 12 semi-structured interviews with policy informers, (driver-)organizers, litigators, and a lawmaker in the rideshare space. We show how data probes act as boundary objects to assist stakeholder interactions, demystify technology for policymakers, and support worker collective action. We discuss the potential for data probes as training tools for policymakers, and considerations around data access and worker risks when using data probes.
Trust in high-profile election forecasts influences the public’s confidence in democratic processes and electoral integrity. Yet, maintaining trust after unexpected outcomes like the 2016 U.S. presidential election is a significant challenge. Our work confronts this challenge through three experiments that gauge trust in election forecasts. We generate simulated U.S. presidential election forecasts, vary win probabilities and outcomes, and present them to participants in a professional-looking website interface. In this website interface, we explore (1) four different uncertainty displays, (2) a technique for subjective probability correction, and (3) visual calibration that depicts an outcome with its forecast distribution. Our quantitative results suggest that text summaries and quantile dotplots engender the highest trust over time, with observable partisan differences. The probability correction and calibration show small-to-null effects on average. Complemented by our qualitative results, we provide design recommendations for conveying U.S. presidential election forecasts and discuss long-term trust in uncertainty communication. We provide preregistration, code, data, model files, and videos at https://doi.org/10.17605/OSF.IO/923E7.
Existing data visualization design guidelines focus primarily on constructing grammatically-correct visualizations that faithfully convey the values and relationships in the underlying data. However, a designer may create a grammatically-correct visualization that still leaves audiences susceptible to reasoning misleaders, e.g. by failing to normalize data or using unrepresentative samples. Reasoning misleaders are especially pernicious when presenting public policy data, where data-driven decisions can affect public health, safety, and economic development. Through textual analysis, a formative evaluation, and iterative design with 19 policy communicators, we construct an actionable visualization design framework, V-FRAMER, that effectively synthesizes ways of mitigating reasoning misleaders. We discuss important design considerations for frameworks like V-FRAMER, including using concrete examples to help designers understand reasoning misleaders, and using a hierarchical structure to support example-based accessing. We further describe V-FRAMER's congruence with current practice and how practitioners might integrate the framework into their existing workflows. Related materials available at: https://osf.io/q3uta/.
Political communication is critical for democracy, but polarized emotions in communication may make careful deliberation difficult. Much of modern political communication occurs on social media, which may exacerbate these challenges. This study examines how the design of social media features impact political communication. We examined how the introduction of Facebook Reactions influenced the posts of the 114th US Congress on the platform. We start by analyzing the emotional content of posts, finding that politicians generally increased their usage of negative emotions in their posts after the feature's launch. Further analysis showed that increased user engagement preceded the rise in negative emotions, suggesting that politicians were making adjustments based on user feedback. Our results show that the design features of social media can shape online political communication.
Election results in the United States are visualized online in real time by news outlets as vote counting persists over days or weeks. They are a massive public-facing exercise in managing audience understanding of uncertainty in partial data, breaking news web traffic records as the public seeks information about winners. We categorize designs of real-time election results from 19 U.S. news outlets and election results providers for the 2020 and 2022 general elections to create a visual vocabulary of live results. We then use this vocabulary to guide interviews with data journalists who worked on these designs to understand their design goals and challenges. Tying these conversations back to our visual vocabulary, we map out how communication goals like balancing certainty and uncertainty in the journey towards finding out winners, alongside challenges like determining thresholds at which information is shown, manifest in the designs displayed.