DIS/POSSESSORY DATA POLITICS: From Tenant Screening to Anti-Eviction Organizing

In this article I place tenant screening data grabbing practices in tension with the ongoing work of housing justice-based tool making. While the tenant screening industry has spent decades amassing eviction data to facilitate the blocklisting of tenants with prior eviction records and thereby reifying racial capitalist geographies, housing organizers today rely on some of this same data to illuminate evictor networks and organize anti-eviction campaigns. This has been particularly important in the wake of corporate landlordism in which evictions are executed through opaque shell companies. Tenant-made tools attempt to undo this uneven landscape in which landlords own troves of data about tenants, but in which tenants don't even know their own landlords’ names. While opening up all eviction data to the public might appear to be an antidote, doing so can also provide screening companies with even more data to use in blocklisting. In my examination of this conjuncture, I forge the analytic of dis/possessory data politics to map out the violence of tenant screening data predation while also problematizing the technoliberal impulse to open up all eviction data. Yet dis/possessory data politics also attest to care and coalition work marked by practices of possession beyond logics of theft, banishment and techno solutionism.     

Mind the gender gap: Inequalities in the emergent professions of artificial intelligence (AI) and data science

The emergence of new prestigious professions in data science and artificial intelligence (AI) provide a rare opportunity to explore the gendered dynamics of technical careers as they are being formed. In this paper, we contribute to the literature on gender inequality in digital work by curating and analysing a unique cross-country data set. We use innovative data science methodology to investigate the nature of work and skills in these under-researched fields. Our research finds persistent disparities in jobs, qualifications, seniority, industry, attrition and even self-confidence in these fields. We identify structural inequality in data and AI, with career trajectories of professionals differentiated by gender, reflecting the broader history of computing. Our work is original in illuminating gendering processes within elite high-tech jobs as they are being configured. Paying attention to these nascent fields is crucial if we are to ensure that women take their rightful place at forefront of technological innovation.