Narrowing Topics and Asking Questions

Here is the introduction and I will attach the topic and the paragraph together.

First, go back to Research Topics: From Broad to Narrow and choose one or two more narrow topics. These should be topics you think you could explore for your final research paper. Note: you’re not required to choose these topics for your final, but it’s a good idea to settle on one now. It means the work you do now will benefit your final project.

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Then, use the advice from The Craft of Research (I will attach it too)
to generate a series of questions about your topic–at least eight specific research questions. See the example below for a guide. Note that you might have to do some preliminary research to ask good, detailed questions. If you found your topic in CQ Researcher or Gale in Context: Opposing Viewpoints, you’ll have enough background information to ask some good, complex questions.

Remember: research questions are complex, open-ended, and challenging. Simple fact-retrieval questions won’t help. If you can answer the question with a quick Google search, it isn’t a good research question.

Lastly, start the process now of identifying important sources. List links here to at least three or four sources you might be able to use for this topic and write a sentence or to about what the source is and how it might help you later on. Again, if you’re getting information from CQ Researcher or Opposing Viewpoints, this won’t be a challenge; there are scores of sources listed in every article.

If you decide to discuss two sources, follow the same process for both. You only have to write eight total questions about both sources, and you only need to list out four total sources.

Here’s the example

An Example First Post:
Finding a Narrowed Topic:

Hopefully you already have a narrowed topic or two in place. If not, here’s an example of narrowing your topic:

Let’s say that I’m interested in artificial intelligence, but I don’t know where to start. So I select an article to get started: “Algorithms and Artificial Intelligence
Links to an external site.
” from CQ Researcher (one of the links from Research Topics: From Broad to Narrow).

Now, “Algorithms and Artificial Intelligence” is definitely not narrow enough! Remembering the advice from The Craft of Research:

It can be stated in four or five words
There are no verbs or words coming from verbs–so there are no words expressing relationships or actions.
It doesn’t suggest any claim or argument

So I have to narrow it. As I read the article, I find an interesting problem: the possibility that algorithms can carry the same biases that humans do. Specifically, the problem of algorithms perpetuating racial discrimination unless they are carefully created and evaluated.

It’s longer and more specific. It expresses relationships and actions (perpetuating discrimination, careful creation and evaluation). And it suggests a claim or argument as well: that algorithms can be just as biases as people are.

This will work really well as a narrowed topic! Now, it’s time to ask some questions. I’ll use what I’ve read in the CQ Researcher article to help out.

Asking Questions:

Here are some questions I might ask, based on the guide to questions I saw in the Craft of Research:

The Parts of my Topic:

– There seem to be a couple of different kind of bias. In one example, a program called Face++ was more likely to incorrectly identify the gender of Black women. This seems to be an example of an algorithm that doesn’t know enough about nonwhite people. In another, the article says that algorithms can lead to discriminatory mortgage lending; this seems to be an example of an algorithm that is directly biased. So here’s my questions: How many of these problems are due to a lack of information, and how many of them are due to actual bias in the algorithm?

– How does lack of information about people of color relate to bias in the algorithms? This is about how the parts of my topic relate to each other.

The History of My topic:

– The history of algorithms (given in the “Chronology”) goes back to the 1930s. But when did we first start talking about algorithms and racial bias?

– What were the first examples of racial bias in algorithms that were identified?

– Who started talking about this problem? Were they listened to? How much response did they get?

– Who developed these early discriminatory algorithms, and what was the relationship between the bias the developers had and the bias in the algorithms?

Characteristics and Categories:

– There’s a section in this article called “Assigning Blame when Algorithms Do Harm.” This is similar to other conversations about AI, such as the worries about driverless cars. What are the similarities between the conversations about discriminatory AI and deaths or harm charged by driverless cars?

– This topic is about AI and computing, but it’s also about racial discrimination, social justice, and racial equality. What can we learn about AI when we connect it to racial equality? What can we learn about racial equality when we connect it to computing?

Saving Sources:

As I read, I found some really important sources that I want to save for later. For now, I’ll just but down links and a few words:

“Joy Buolamwini, How Does Facial Recognition Software See Skin Color?” TED Talks, NPR, Jan. 26, 2018,
Links to an external site.

– This is a really important source. A lot of the quotes in the article about AI and discrimination are from Buolamwini.

Steve Lohr, “Facial Recognition Is Accurate, if You’re a White Guy,” The New York Times, Feb. 9, 2018,
Links to an external site.

– This article talked about the Face++ program that misidentified the gender of Black women.

Aaron Glantz and Emmanuel Martinez, “Detroit-area blacks twice as likely to be denied home loans,” Detroit News, Feb. 15, 2018,
Links to an external site.
; Virginia Eubanks, “The dangers of letting algorithms make decisions in law enforcement, welfare, and child protection,” Slate, April 30, 2015,
Links to an external site.

– These two sources were listed together, and they both talked about an AI that was used to rank people as “Deserving” or “Undeserving” / “Suspicious” and “Unsuspicious”

“Artificial Intelligence and Life in 2030,” Report of the 2015 Study Panel, Stanford University, September 2016, p. 49,
Links to an external site.

– This source asked if AI can “intend” to commit a crime. This seems to be an important question for my research.

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