Big data surveillance turns on low-income communities
In September 2016, the US Federal Trade Commission hosted a workshop to study the impact of big data analysis on poor people, whose efforts to escape poverty may be hindered by the extensive amounts of data being gathered about them. Among those who intensively surveil low-income communities are police, public benefits programmes, child welfare systems, and monitoring programmes for domestic abuse offenders. Some areas require applicants for food stamps and other public benefits to undergo fingerprinting and drug testing, and their spending and living environments may be monitored if their applications are successful. The data from this monitoring may flow back into police systems and close off future opportunities. For example, recruitment systems may automatically exclude anyone who has had contact with the police, up-ending the presumption of innocence. Even expunging arrest records doesn't always help, as errors and old information may persist in other databases that have received the data as part of a sharing arrangement - and there, the person affected can't gain access to correct them.
The FTC workshop also found a number of areas where big data can be used to help low-income communities, such as using alternative information sources to gain better access to credit for people who would not have credit scores from more traditional sources.
Writer: Kaveh Waddell