The course is an introduction to data science for students from all fields of study, and students get hands-on experience working with data, which includes considering important questions about how data is collected, what data is collected (or not), what that data can’t tell us, and how data can be misused, as well as how it permeates daily life. Students wrote short essays on their thoughts for the class. Here are three that show the range of topics addressed:
by Rachel Okin
Since learning about statistical inferences in class and also starting to work on my group project, I've run into limitations of statistical inferences that may cause ethical issues in my project. For the sake of this blog post, I will rephrase our group project question to one that better reflects a probability, such as: "What is the probability that a student who comes from a family with a higher income bracket will make greater earnings (than a student from a lower family income level) when both students are 20 years out of university, if both students went to a top tier university?" For this question, the population would be the millions of students who attend colleges or universities throughout the US and these students' families, but with the dataset we've chosen, the sample size is only ~1515 students. One limitation of using statistical inferences in this case is that we are using a relatively small amount of data to make conclusions about a population that is magnitudes greater. I wonder if our small sample is skewed or if our distribution would look completely different if we had 1000 more subjects or even 1,000,000 more?
Another ethical issue that comes with using statistical inferences for our specific college and university dataset is that the subjects are anonymized even though each individual subject is incredibly different. In this dataset, the only substantial information we know about the anonymous subjects is the tier of school they attended, their family’s income level, and their “earnings” when they left college. There are many more substantial factors about these individuals that, if recorded, could help us make more informed conclusions about the population, such as where each subject grew up, how many siblings their parents were providing for, or potentially if they’ve encountered impediments in their job search. Because the world we live in is full of biases that may determine who gets a job or how much one person gets paid in comparison to another person, we must evaluate each individual’s situation separately. Therefore, it feels ethically wrong to come to some conclusion about the probability of an individual to come out of X tier college with X earnings because their parents were of X income bracket when we are completely disregarding the various other reasons for why or how that individual has that outcome. While we cannot always realistically collect data for and study every single subject in a population of millions, we must recognize that the alternative, using statistical inferences, also often has ethical flaws.
by Katie O'Leary
A friend of mine made a comment the other day. As she was searching through her phone she made an off-hand comment “Do you ever start closing your apps and then realize how many are actually there?”
No. Actually, I don’t. Maybe it’s a difference in personality, maybe in generation, or maybe it’s a new-found awareness in myself. I tend to be on the cautious side, and so my location is turned off, I close my applications after using them, and always manage my cookie settings when I enter a website. That’s my normal for managing my data.
On the other hand, my friend recently mentioned that an ad came up for a candle she had been talking about. This data harvesting from audible conversations is real, and can make some people uncomfortable, but depends on those settings you read (but really didn’t) and accepted. She had also searched for the candle on her browser and opened a link to the seller’s website. She gets targeted for ads and that’s her normal for managing her data.
Neither approach is inherently better, but these interactions start a dialogue about what happens to our data and who we let collect it. The choices themselves are important, but not as critical as the ability to have the conversation in the first place. It all starts with honesty.
I firmly believe that honesty and transparency are the pillars of ethics in data science, and something that I work hard for in my own data projects. Knowing that I have to explain my choices in data science makes them more thoughtful. When data analysts, programmers, and scientists are honest, end-users attain self-actualization and personal choice. Honesty empowers. Data is given – not taken. The users are given the dignity of knowing, which lets them calculate their own data risks and prevents misuse.
In the end, we’re all learning and working through the digital age together, making our choices, and learning how to interact with data collection and use. The issues of ethics in data science is a continuous conversation, but end-users need honesty from data scientists in order to understand the choices they have. Plain-language honesty is paramount. It allows myself and my friends to be empowered by choice and spurs ethical thought in data scientists.
by Isaiah Spencer
The most prominent example I have seen in the media that has shown the disregard for data ethics, which was not only brought up in this class but also at my internship at the Rhode Island Department of Health, was the misinterpretation of COVID-19 data in the state of Florida in order for businesses to open earlier. Rebekah Jones is the GIS analyst who was told to show the data in a way that would convey that it is safe to reopen the state of Florida. She rightly rejected their proposition and was eventually fired for her actions. Hearing about this made me upset, saddened and confused that even with a pandemic, organizations disregarded proper data ethics in order to immaturely reopen the state faster. Rebekah Jones was a keynote speaker at an event I attended called NEURISA Day 2020. Overall, her talk was enlightening and disheartening, specifically on the relationship between the data scientist and the political leaders. She went into more detail on how the Florida Department of Health went about wanting to manipulate the COVID-19 data. The "leaders" of the Florida DOH wanted to change the data in order to line up with their reopening plan rather than allowing the data to provide the information for why they should not reopen early. Even before the pandemic was serious in the United States, Rebekah wanted to inform the public on the seriousness of the disease using GIS. However, Florida's DOH did not want to go ahead with this until it got serious because a) "the public didn't need the information" and b) "they didn't want to cause a panic." The most striking comment she made throughout her whole talk was this: "people didn't need to die, and they did." That last statement from Rebekah Jones sums up the importance of data ethics in all organizations.