In April 2020, the Colombian government announced an unconditional cash transfer program to provide income to families who were deemed vulnerable to economic shocks caused by the pandemic. A mere 2 weeks after the scheme was launched, 3 million families were notified by SMS that they had been selected as eligible and received the funds directly into their bank accounts. Rather than having those in need apply themselves, the government independently chose eligible beneficiaries. The government did this by combining as much data as possible about its citizens, building a new master database from 34 public and private databases, including from private credit bureaus. This data had been collected for completely different purposes and, in some cases, without the knowledge of the included individuals. The master database was used to predict ‘income-generating capacity,’ using a secret algorithm, and thereby automatically determine who would be deserving of additional financial support during the pandemic. The Colombian approach poses a range of questions that are relevant far beyond its borders. Did it work? Who ultimately decided who was eligible, and might such decisions be democratically controlled? Is this a move away from a neoliberal model of ‘targeting’ social benefits, or its continuation in different form? What model of ‘datafied’ government is Colombia building within its welfare system, and how will it affect the human rights of the most marginalized?

Speaker: Joan López, Researcher at the Global Data Justice Initiative and at Colombian NGO Fundación Karisma


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