In light of the recent election, it is more important than ever to look at how and where we are responsible for perpetuating prejudice. In a previous article, DataCrunch introduced the concept of “Weapons of Math Destruction,” which are data models built from a limited or biased sample of data that result in toxic feedback loops. Since this explanation is most often attributed to artificial intelligence, there is little discussion about how this description could also illuminate the workings of the human mind. While many might want to think of this narrow-mindedness as below the mental capacity of human beings, such a viewpoint is dangerous in that makes having a conversation about prejudice difficult.
I’ve feared the moment that my summers would be turned over to internships for a long time. I can’t remember for how long I’ve known internships are important – probably for as long as I’ve known about applying for college. My relationship with the idea of internships has gone through stages, with me sliding from thinking that they are silly resume builders to valuable and necessary work experience almost every day. I recently decided that I wanted to pursue some sort of consulting internship, and then felt a drop in my stomach similar to when I decided to apply for Wesleyan. But while there is a large and personalized application process still ahead of me, I don’t want to feel as scared as I did then. With this in mind, I sat down with Asie Makarova ’17 and Taylor Chin ’18 to discuss two of the main myths about internships and what truths, based on their experience, lie beneath.
In 1994, a small company called Marvel acquired the rights to sell children’s toys and comic books based off of their characters. During this time they were riding the wave of the comic book boom, a time when comic book consumption and production reached a sudden high. Marvel entered this period of success with high hopes, and followed the lead of other comic book companies to find success. This follow-the-leader approach turned against them when the market collapsed in 1997, forcing Marvel to declare bankruptcy.
All of this happened before Marvel Entertainment was the media power house we know today. Now, it seems as if Marvel is expanding into every corner of product design, churning out movie and TV series with a built in comic book and merchandise market at such a pace that some are calling this Marvel’s Golden Age. This approach is startlingly different than the company’s mantra in 1997, leading many Marvel enthusiasts to ask themselves what has changed between then and now.
This article is inspired by and quotes from Weapons of Math Destruction by Cathy O’Neil, a book about O’Neil’s growing disillusionment with the data economy as she learned that data can be used to fuel toxic feedback loops. This post is the first in a series DataCrunch will be doing based on the examples cited in her book.
When preparing to apply to college, one of the first references that people often turn to are lists of college rankings. Almost every newspaper/journal has one – Forbes, Princeton Review, U.S. News. They are a big deal within higher education, with students and parents often referring to the lists as a point of reference when choosing where to apply. But the scope of influence goes beyond that. Alumni and teachers will also look at these lists to decide if they want to apply or donate money. These simple rankings of colleges have become somewhat of a bible in higher education that destines a school to fly or flop – all based on what their ranking is.
Does this sound scary to you? It should. It’s hard to truly understand the amount of power we give to these lists until you step back and look at how far the cycle of impact spans: The process of applying for college has become so much more than just “applying.” High schools will start prepping students their freshman year to be wary of their grades, ranked GPA, AP scores, extracurriculars, volunteer work, honors society, SAT scores, ACT scores…. And when high schoolers are stressing out about how much there is to do, they surely don’t think back to those college rankings that they started reading with your parents for fun. But the truth is that they are the center point of a vicious feedback loop that now controls our higher education system.
In case you have not noticed from the multiple TV ads, for a few years now IBM has been positioning itself as a Big Data company, with its Watson platform and cloud-based services. One of them is the Alchemy Language API, which packs together functions for text analysis and information retrieval. As part of learning how to handle this API from R, I tried it on a news story about a sci-fi book publishing business. Overall, the results were strong, although not without some amusing quirks…