Response questions: Joshua Blumenstock states that a humbler data science could transform international development while also limiting the number of alleged silver bullets that have missed their mark in recent years. Describe the promise, pitfalls and ways forward Blumenstock uses as the foundation for his thesis. Additionally, consider the following statements from three of your classmates regarding this article. (1) "Good intent is not enough in data science when dealing with the problems which determine people's experiences" Anna Raymond (2) "Transparency is the underlying issue to many of these problems, so an increase in this on both ends (data based issues & human based issues) could lead to better results." Nira Nair (3) "In lieu of such drastic potential for promoting applications yet demoralizing hinderances, the balancing act can become difficult." Kayla Seggelke How do you respond to these ideas regarding "good intent", "transparency" and the difficult "balancing act" when considering the intersection of human development with data science?
Throughout the article “Don’t Forget People In The Use Of Big Data For Human Development”, Blumenstock proclaims that the current system for actively helping nations and citizens experiencing levels of poverty are lacking not only in execution, but also efficiency due to the current data science methodologies focusing on the “big scheme of things”, rather than the basics.
In terms of the “lacking in execution argument” that he brings up, author Blumenstock explains that the motives of researchers who want to aid in human development for poorer countries are “full of promises”. As seen towards the beginning of the article, machine-learning algorithms provide data like credit scores to people who have mobile phones but might lack other ways of getting a credit score, such as having collateral or bank access. An example of lacking in credit occurs in paragraph 10, a story relating to the citizens of Kenya: “Take the example of ‘digital credit’. Would-be borrowers are assessed using credit scores based on their history of phone use, and loans are dispatched instantaneously by mobile phone. A booming industry has developed since the first such service, M-Shwari, was launched in Kenya in 2012. Banks, phone companies and next-generation financial-service providers collectively make hundreds of thousands of loans per day in sub-Saharan Africa alone. Today, more than 25% of the Kenyan population has taken out at least one digital loan” (Blumenstock 10).
While it is clear there are major pitfalls regarding the effectiveness of big data being used to help development, Blumenstock provides a unique approach forward as a rudimentary solution to the issue of “lack of representation” for those who need it. Blumenstock separates his ways forward into 3 different categories: Validate, customize, and deepen collaboration. In terms of validation, we must create data sources to replace outdated ones and promote collaboration. In terms of customization, we need to customize data packages so they are turley aiding those who need it. Lastly, in terms of collaboration, Blumenstock states “One way to achieve this is to foster collaboration between data scientists, development experts, governments, civil society and the private sector” (Blumenstock 29). Overall, collaboration is going to be the most important aspect of these ways forward, as Blumenstock continuously emphasizes.
In terms of the topics of transparency, good intent, and the balancing act, each is unique in its own way, yet they are all additionally similar because they are interrelated. In terms of transparency, it is not always the case that it can lead to better outcomes. Blumenstock explains that when people know data is being collected in relation to themselves, they are more likely to “game the system” which overall skews the statistics results and what is perceived by data scientists to be accurate.
Lastly, in relation to the “balancing act”, Blumenstock addresses how big data can have potential sources of bias as a result of undercoverage (a statistical term where certain members in society are not accurately represented), but by accounting for the undercovered population through methods that are currently in place like surveys, balance when considering the intersection of human development with data science is possible.