Feminicide & machine learning : detecting gender-based violence to strengthen civil sector activism

Although governments have passed legislation criminalizing feminicide, it is unaccompanied by relevant policy or robust data collection. This participatory action research project is designed to help sustain activist efforts to collect feminicide data through partially automated detection using machine learning. As a way to counter the impunity surrounding feminicide, activists have taken upon themselves to do the work that states have neglected. Partially automating detection supports efforts to systematize and sort data collection across contexts, and helps to inform policy advocacy through standardizing definitions and taxonomies. The ability to prioritize articles by likelihood of feminicide will make this intense research less gruelling

Race-neutral vs race-conscious: Using algorithmic methods to evaluate the reparative potential of housing programs

The racial wealth gap in the United States remains a persistent issue; white individuals possess six times more wealth than Black individuals. Leading scholars and public figures have pointed to slavery and post-slavery discrimination as root cause factors and called for reparations. Yet the institutionalization of race-neutral ideologies in policies and practices hinders a reparative approach to closing the racial wealth gap. This study models the use of algorithmic methods in the service of reparations to Black Americans in the domain of housing, where most American wealth is built. We examine a hypothetical scenario for measuring the effectiveness of race-conscious Special Purpose Credit Programs (SPCPs) in reducing the housing racial wealth gap compared to race-neutral SPCPs. We use a predictive model to show that race-conscious, people-based lending programs, if they were nationally available, would be two to three times more effective in closing the racial housing wealth gap than other, existing forms of SPCPs. In doing so, we also demonstrate the potential for using algorithms and computational methods to support outcomes aligned with movements for reparations, another possible meaning for the emerging discourse on “algorithmic reparations.

Seeing like a driver: How workers repair, resist, and reinforce the platform's algorithmic visions

This article theorizes the relationship between two ways of “seeing” and organizing urban mobility markets: the abstract, algorithmic vision of the mobility platform and the experiential, relational vision of the platform driver. Using the case of mobility platforms in Jakarta, we empirically demonstrate how drivers experience the limitations of the platform's visions and how they deploy their own alternative visions of work and the city. We offer this drivers’ “View from Within” as a counterpoint to the visions of the platform, decentering the platform's visions as the sole arbiter of change and optimization in the city. At the same time, we disrupt the assumed binary between these views, showing how they exist in a complex dance of complementarity and contestation. We conclude with a discussion on the opportunities this entanglement presents for worker agency in the algorithmic market, the hurdles toward more “worker centered design” in platform economies and the tensions between globalizing technological solutions and their localized instantiations. Through this article, we argue for seeing deep, embedded relationships as culturally and historically important modes of urban life which technology has to interact with but cannot fully capture nor do away with. </jats:p