Hi everyone! I happily joined the class last week, so I will combine post 1 & 2 here.

I’m Julie and I’m currently based at the Harvard Humanitarian Initiative, as part of the think tank Data-Pop Alliance co-created by Sandy Pentland and Patrick Vinck (HHI, HSPH), where I lead applied research projects around the use of big data/AI for public policy and development – with a strong focus on the LATAM region.

I studied in France, where I graduated from two masters in (1) Gender Studies (Université Paris Diderot – VII) and (2) International Development (Sciences Po Paris). My program in Gender Studies (officially in “Gender and development” at the time), gave me (much needed) materialist-intersectional-feminist views about the “development field” that had a strong impact on both my studies and my personal socio-political perspectives.

So it is through a “development” approach that I got into the “tech for social change” field. The projects I am currently involved in look into leveraging CDRs and bank data to study crime dynamics in Colombia (focus on drivers of criminality) and Mexico (focused on the impact of crime waves on citizens’ routines).

Project reflection:

The following is not (yet) an implemented project – but one at the stage of conception being actively discussed with a Media Lab PhD student.

When I moved back to São Paulo in 2016, I volunteered at the National Committee for Refugees (the governmental body in charge of deciding on all asylum claims in Brazil). At the time, Brazil was receiving an increasing number of asylum applications, notably from Syria, as the country had adopted a “laissez passer” policy for Syrian nationals. There I worked as an interpreter for interviews but also answered (or tried to answer) the questions from asylum seekers and refugees that would come asking about procedures or oftenly to check on the status of their applications – which were taking months (sometimes up to 1-2 years). In 2017, I was part of a small team that conducted surveys in a Syrian refugee camp in Greece. Most of the people in the camp, had done their asylum request and had been waiting for several months already to hear back about their status – and their new papers that should allow for the start of a new phase in their lives. I vividly remember a young refugee telling us “Why are you studying our use of mobile phones? What we really need is to get out of here, study, work, continue with our lives”.

To summarize the scope of the problem: in both countries, and anecdotally in others, asylum services are extremely lengthy, frustrating and adding to the hardship of people that are eager and impatient to start building a better life. Part of the problem is surely that asylum services are overwhelmed with more demands than they were/are equipped to handle (in Brazil, we were a team of 20 volunteers, also doing contextual background checks to contribute to the asylum request processes). Bearing that in mind, we are exploring the idea of using an algorithm, built to improve the targeting of social programs, in a stage of the asylum procedure, with the goal to expedite the process.

The ideal person to address this issue would likely be a specialist of the legal and procedural asylum process (in each country of implementation) along with a data scientist well versed on algorithmic fairness and bias.

The first potential consequence harmful consequence I imagine could come from systematically unfair predictions – as certain cases will be easier to ascertain (for e.g. citizens from a war zone, such as Syria) than others (for e.g. an individual being politically persecuted in a “peaceful” country).