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. How do you response to these ideas regarding “good intent”, “transparency” and the difficult “balancing act” when consdiering the intersection of human development with data science?
There is no doubt that data and its implementations are becoming more and more relevant in our day-to-day lives. I always believed that data science was only limited to making user experience better on certain apps, such as Facebook or Amazon. However, reading this article opened by eyes to the true potential of data science. I had no idea that data analysis could be used to pinpoint impoverished regions in Africa, in order to distribute humanitarian aid efficiently. Whether it is helping fight the pandemic or lending loans to people desperately in need of financial assistance, it is clear that data science is an efficient way to do these things.
However, just like many things in the world, there is always bad that comes with the good. As stated in the article the pitfalls of data analytics boil down to 4 things: Unanticipated effects, lack of validation, biased algorithms, and lack of regulation. The example of administering loans to people who seem to have decent credit scores, led to a bigger issue of many Kenyans not understanding that they had to pay interest payments. I feel that data science sometimes portrays aid or financial help in too good of a light, without truly painting the picture of the terms and conditions that apply with that aid. I do feel like as these projects to help people need to be more well thought out, in order to prevent the cycle of poverty and debt traps as discussed in the article. Lack of validation and biased algorithms have always been a problem with statistics in general, yet I feel that once data science gains more traction, it will not be as much of a pitfall as it is now. Finally, the most important pitfall that many view as data sciences’ most detrimental drawback: lack of regulation. It is hard to make sure that all data being collected is used in a just and right way, and I feel that there should be a sort of UN (United Nations) for data analytics.
I do agree with the statement that “Good intent is not enough in data science when dealing with the problems which determine people’s experiences”. The perfect example of this is the loan payments given out in Kenya. There was good intent with the mission to aid impoverished Kenyans; however, it led to even more poverty and debt traps. Good intent means nothing without proper execution. Now regarding transparency, I have a very unorthodox way of thinking about this issue. I do not think that there should be as much transparency with the collection of data (with the exception of private photos and videos). My thinking is that everyone knows their data is being collected with applications such as Facebook and Amazon, and we all just accept the fact. It is clear in most cases it just makes the user experience better; however, if people were to be given a list of everything being tracked, they would automatically say no. This response would hinder any more potential growth in the world of data science. I do understand the struggle of balancing all these moving pieces that come with data science, ; however, our society is showing that data is becoming more and more important. Data just existing does nothing for human developing, but when it is analyzed and refined, it can lead to human development that is unbelievable.