Burcu Baykurt Co-Authors Research Article on Predictable Borders

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Burcu Baykurt, communication, recently co-authored an article with Alphoncina Lyamuya '21 UMass MPPA and PhD student at University of Southern California, focused on the use of machine learning and predictive technologies to determine border access by bureaucratic institutions. 

"From concerns around the ethics of consent for data collection to data security to racial and gender bias and discrimination in decision-making, predictive analytics in migration presents a range of significant risks and harms. It is also technically too complex to build and maintain a predictable model of mobility given the uncertainty and diversity of mechanisms resulting in forcibly displacing people worldwide. Against these risks of success and potential harm, we ask: how does a humanitarian agency keep the idea of a predictable border resilient when it does not work as intended or risks harming the people it aims to protect?" writes Baykurt and Lyamuya. 

Read Making up the predictable border: How bureaucracies legitimate data science techniques