Data and inequity

The capacity to gather, analyse and use data through sophisticated computational techniques like big data analytics and machine learning, combined with digital surveillance represents significant social, political-economic, and technological developments that have become ubiquitous in ordering our lives. Data can be used toward potentially discriminatory or unethical purposes with the potential to exacerbate social inequalities among vulnerable communities. Furthermore, the combination of “technical complexity” and “corporate secrecy” means that the opacity of both algorithms and the data shaping them are ‘black-boxed’ leaving the public with little recourse for potential harms (Pasquale, 2015). Consequently, how these systems transform or exacerbate social inequalities and power differentials in a data-enabled society is an important scholarly concern. In November 2018, I co-organised a symposium about data and inequity hosted by The Data, Systems and Society Research Network (DSSRN – pronounced discern). The purpose of the symposium was to think critically through the implications of data-driven inequality and discrimination: both across disciplinary boundaries in the academy, and within diverse empirical domains outside of it through three key areas: Indigenous people, cities, health concluding with an exploration of the ethical frameworks available to navigate issues of inclusion, exclusion and surveillance. You can read the interdisciplinary collection of peer-reviewed papers: Data and inequity: Who’s missing in big data? Edited by Ruth De Souza or download it. Grateful to Debris Facility PTY Ltd for the images, Abbra Kotlarczyk for copyediting and peer-reviewers: Ximena Comacho, Donna Cormack, Angela Daly, Julie McLeod, Gerard Goggin, Monique Mann, Jack Nunn, Michael Rigby, Liz Sonenberg, Chen Zong