Blumenstock Article Question and Response
Question:
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.
“Good intent is not enough in data science when dealing with the problems which determine people’s experiences” Anna Raymond
“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
“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?
Response:
Joshua Blumenstock displays a multitude of strong arguments in this article. In a nutshell, he explains that big data used to improve our daily lives also has very far-reaching capabilities when applied to human development. However, there are many pitfalls that he discusses. Most captivating is how big data tends to aid the wealthy much more than those who could truly benefit from its abilities. Another large problem is how these applications of big data have not been tested or validated enough to this date. They are applied as if they have been. This is a huge problem; it can create misleading or even incorrect information. Blumenstock raises many solutions to these problems. For example, he emphasizes the spirit of collaboration within the use of big data. Data science is indeed a collaborative science, and should not be restricted to those who have access to smartphones or laptops. More importantly, it should be available to those people who work with data science for the betterment of humanity as a whole, rather than aiming for profit.
The whole point of data science is to use a large magnitude of data to improve everyones’ lives. Good intent does not always lead to this. Intent has the connotation “I tried to be a good person” rather than “I am being a good person”. Another argument is that “good intent” is a very subjective idea. What deems an intent “good”? Who’s to say?
While I agree that transparency in data science is the problem, I would also argue that it is the solution. I believe that the ideas of “good intent” and transparency happen to intersect when there is more than enough transparency. Full transparency leaves little room to have malicious intent. Hopefully, transparency can lead to the discovery of the true virtues behind data science and how it can be applied to further our species in this world. In continuation, full transparency paired with good intent will lead to the development of solutions to worldwide problems through the use of big data.
The idea of the “balancing act” challenges the use of data science for human development. While everyone gets so excited to use big data for these new projects, they tend to forget the side effects it may have. Blumenstock explores this idea on page two of his article “Don’t forget people in the use of big data for development”. In addition, this balancing act seems to be rather low on the totem pole of some data scientists’ worries. For example, small startups, such as those in Silicon Valley, tend to focus on securing funding and using these investments to lead to more profits. Therein lies the problem. Before a small startup can focus its efforts on helping those in need, they must first secure their place in the Valley, by scoring a consistent flow of revenue. Once the money really starts flowing, it can take over the startup’s initial focus and become the number one priority. The effect that this has on the use of data science for human development is that the wealthier countries are profitting more than those countries that are crying out for help.
Class Notes:
What is Big Data?
- Call Detail Record (CDR): everytime we use our phones, these actions are recorded and mined for information
- Time: proxy of income
- Who these calls/texts go to
- Location
- Together, these provide a dynamic description of every cell phone user.
- Satellites
- Recording time and location (dynamic)
- Remotely sensed data (raster images)