In Joshua Blumenstock’s article on big data, the intentions of the people behind the scenes seems to be emphasized in the world of data science. However, he also makes a key point in that people should know more about the context of data science and how algorithms without customization could lead to exacerbating consequences in developing countries.

Before delving into the pitfalls of data, Blumenstock writes about the blueprint that it provides in an effort to aid marginalized populations around the globe. This is demonstrated by his inclusion of research in cell phone data and how it is incorporated with statistics that deal with debt payment and likelihood of people spreading disease after recovering from natural disasters and migrating to different areas. Earth-imaging methods used by agencies are used to detect populations that have suffered from these devastating natural disasters, which help humanitarian organizations send relief funds to these areas more quickly and efficiently. However, Blumenstock emphasizes that this “promise” is very tempting, but may not always be in the hands of faithful national organizations that have a duty to their people. It has been evidenced through data that some international regimes, such as that of China, use social credit scores to discriminate against citizens using public methods of transportation. Furthermore, the promise of having an efficient, technologically advanced society is hindered by the fact that developing countries may not have access to data, let alone electricity to power these data-producing devices. These issues can be easily overlooked by the data scientist that refuses to acknowledge this and continues to use objective algorithms that do not accommodate for a country’s traditions and technological prowess.

To address these inevitable challenges in the world of data science, Blumenstock proposes several methods that can be used to tackle them. For example, the use of data science validation would allow for a safer practice of data science as it would potentially lead to new ways of collecting data. The idea is to only come up with ways to enhance conventional data methods, not completely get rid of them. This is crucial to data scientists as these foundational methods will always find use in the world of data collection and research, which is illustrated by the study in Haiti data collecting in which both surveys and phone data collection were used simultaneously to identify trends.

Human development is undoubtedly directly correlated to human advancement in technology. As explained in the article, education and power is only in the hands of a few in many nations, which can cause widespread corruption. To answer the question of having good intent while also being transparent and balanced with data, experts in this field should branch out of objective algorithms and take into account the social context of the areas they are trying to help, even when they have good intentions. If this is neglected, it will perpetuate the notion of Americanization, which discourages the need for global collaboration in the field. Overall, data scientists should apply what they learn to social movements to foster a more inclusive practice in the field, which can help alleviate the misrepresentation of many marginalized communities.