Scholars and practitioners in public health and social welfare increasingly recognize the need for preventative interventions that address root causes rather than respond to emergent crisis. However, they face significant challenges in designing tools and demonstrating success for these initiatives. We characterize these crucial, but difficult to develop and scale solutions, using Dan Heath’s term “upstream work”. We then explore design solutions to support upstream work through a multi-phase co-design process to assist fire departments developing alternate EMS response programs to reduce 911 call volume. We contribute to literature on designing to support data practices in community organizations and further delineate the key challenge of these programs as upstream initiatives: demonstrating success to stakeholders. We then present our co-designed prototype, a data dashboard to make the promising work of preventative programs visible for different stakeholder audiences. Finally we reflect on good practices for designing to support community based upstream initiatives.

Public records requests are a central mechanism for government transparency. In practice, they are slow, complex processes that require analyzing large amounts of messy, unstructured data. In this paper, we introduce RequestAtlas, a system that helps investigative journalists review large quantities of unstructured data that result from submitting many public records requests. RequestAtlas was developed through a year-long participatory design collaboration with the California Reporting Project (CRP), a journalistic collective researching police use of force and police misconduct in California. RequestAtlas helps journalists evaluate the results of public records requests for completeness and negotiate with agencies for additional information. RequestAtlas has had significant real-world impact. It has been deployed for more than a year to identify missing data in response to public records requests and to facilitate negotiation with public records request officers. Through the process of designing and observing the use of RequestAtlas, we explore the technical challenges associated with the public records request process and the design needs of investigative journalists more generally. We argue that public records requests represent an instance of an adversarial technical relationship in which two entities engage in a prolonged, iterative, often adversarial exchange of information. Technologists can support information-gathering efforts within these adversarial technical relationships by building flexible local solutions that help both entities account for the state of the ongoing information exchange. Additionally, we offer insights on ways to design applications that can assist investigative journalists in the inevitably significant data cleaning phase of processing large documents while supporting journalistic norms of verification and human review. Finally, we reflect on the ways that this participatory design process, despite its success, lays bare some of the limitations inherent in the public records request process and in the ''request and respond'' model of transparency more generally.

Algorithms for home location inference from mobile phone data are frequently used to make high-stakes policy decisions, particularly when traditional sources of location data are unreliable or out of date. This paper documents analysis we performed in support of the government of Togo during the COVID-19 pandemic, using location information from mobile phone data to direct emergency humanitarian aid to individuals in specific geographic regions. This analysis, based on mobile phone records from millions of Togolese subscribers, highlights three main results. First, we show that a simple algorithm based on call frequencies performs reasonably well in identifying home locations, and may be suitable in contexts where machine learning methods are not feasible. Second, when machine learning algorithms can be trained with reliable and representative data, we find that they generally out-perform simpler frequency-based approaches. Third, we document considerable heterogeneity in the accuracy of home location inference algorithms across population subgroups, and discuss strategies to ensure that vulnerable mobile phone subscribers are not disadvantaged by home location inference algorithms.

In the United States, public defenders (lawyers assigned to people accused of crimes who cannot afford a private attorney) serve as an essential bulwark against wrongful arrest and incarceration for low-income and marginalized people. Public defenders have long been overworked and under-resourced. However, these issues have been compounded by increases in the volume and complexity of data in modern criminal cases. We explore the technology needs of public defenders through a series of semi-structured interviews with public defenders and those who work with them. We find that public defenders' ability to reason about novel surveillance data is woefully inadequate not only due to a lack of resources and knowledge, but also due to the structure of the criminal justice system, which gives prosecutors and police (in partnership with private companies) more control over the type of information used in criminal cases than defense attorneys. We find that public defenders may be able to create fairer situations for their clients with better tools for data interpretation and access. Therefore, we call on technologists to attend to the needs of public defenders and the people they represent when designing systems that collect data about people. Our findings illuminate constraints that technologists and privacy advocates should consider as they pursue solutions. In particular, our work complicates notions of individual privacy as the only value in protecting users' rights, and demonstrates the importance of data interpretation alongside data visibility. As data sources become more complex, control over the data cannot be separated from access to the experts and technology to make sense of that data. The growing surveillance data ecosystem may systematically oppress not only those who are most closely observed, but groups of people whose communities and advocates have been deprived of the storytelling power over their information.