In the passage of Joshua Blumenstock, when talking about the development of data science, there are several issues that need to be addressed. There is no debt that the promise of it is to make the society better, especially from a humanistic perspective. However, the pitfalls and potential problems cannot be ignored.
Although the goal of data science is clea for human beings, the pitfalls and potential problems may guide the whole process in very different ways. For example, the database of the algorithm actually can be biased. Since it is collected through a specified media, like phone or internet, it sometimes ignores some groups of people who may not be able to get into the pool. It is possible that the algorithm treats people from different groups unfairly. And the more rigorous question is that the data can lead to preconceived ideas. Just like the Ted talk says, people are used to using average number to see things. However, it has a great probability to ignore the differences inside the population.
First of all, although good intent does play a significant role in the development of data science, it is not enough. The problems that deal with determining people’s experiences can be complex. There are many more elements that all change the predicted model. In order to make sure data’s accuracy and effectiveness, more information is needed. So it leads to another underlying point that is associated with transparency. It is for sure that increasing transparency can lead to a better result, however, based on different moral standards, this goal seems hard to achieve. People especially from different cultural backgrounds may have very different tolerability of informational transparency. I agree with the statement that increasing transparency is an effective way to develop data science. However, considering the intersection of human development and data science, I think the “balancing act” will be extremely difficult to achieve. It is very clear that some western culture based countries show an obviously low tolerance for data transparency compared to some Aisan countries. Which means that those actions that work in one country can not work in another one.
Also, considering those problems are mostly cultural based, it will be very hard to solve. It seems impossible to create a standard that is accepted by all nations around the world that is based on different cultural and moral standards.
That is also why the author holds the idea that data science should be humbler. In my opinion, data science and algorithms are indispensable tools in future human development. That is also why we need to be humbler. It is true that the development of data science brings benefits to a lot of people. But at the same time, because of that many people become unseen and their voice been unheared. We should take it more seriously and careful to avoid those potential problems. In order to ensure the benefits of it to people. We should try our best to ensure everyone’s rights and benefits under the world of big data. We should applied data science as a tool to help us see the huge picture of human deveopment instead of narrowing our sight.