Homework 8: Analyzing Data Bias

Using Matplotlib

Due: 11:55pm (Pacific Time) on Wednesday, March 9th

Based on problems by Colin Kincaid, Monica Anuforo, Jennie Yang, Nick Bowman, Juliette Woodrow, Chris Piech, Mehran Sahami, and Kathleen Creel.

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To get started, download the zip file for HW 8 here.

In this assignment, we have done all of the file reading and data processing for you. We have organized the data files and stored them into a dictionary (the structure of which is described below). In this assignment, you will write code to plot the data in an interesting way. Plotting and visualizing across professor gender and review quality reveals interesting trends about human language usage. We hope that you will be able to use this exercise in data visualization to also think critically about the underlying biases that exist in online datasets! The end product of this assignment is a complete application that will help you dig deep into our provided dataset while answering important social and ethical questions along the way.

Main Program: Analyzing Data Bias

Introduction

In this assignment, you will use your nested data structure and new matplotlib skills to build your very own data visualization application. You will analyze a historical dataset consisting of nearly 20 years of reviews of college and university professors posted on RateMyProfessors.com, a popular review aggregation website. Teacher ratings are a common and impactful facet of life in university – here at Stanford, we fill out course reviews at the end of every quarter. Future students use the results of these reviews to help them choose their classes and plan their academic futures. However, teaching evaluations are not an objective source of truth about the quality of a professor's teaching. Recent research has shown that teaching evaluations often demonstrate harmful biases, including gender bias. The bias in teaching evaluations is a problem because the scores are often used in decisions about who to hire, fire, tenure, and promote. Your goal is to build a piece of software that helps you investigate and reason about how humans use language in gendered (and potentially biased) ways. Here are two screen shots of the program you will build:

First when exploring the word "class":
The x values are [0, 1, 2] which we have turned into labels "Low Reviews", "Medium Reviews", "High Reviews".
The y values for women are [4856.251650382888, 3515.315553208344, 11329.87853181938]
The y values for men are [4161.032213925694, 3543.692174480071, 10871.598616730716]

Second when exploring the word "funny":
The x values are [0, 1, 2] which we have turned into labels "Low Reviews", "Medium Reviews", "High Reviews".
The y values for women are [49.51148666490626, 90.77105888566146, 651.9012410879324]
The y values for men are [93.53636961297322, 299.3163827615143, 1101.0566937298563]

Before we get started coding, we first want to provide you with some background about why being able to investigate and identify biases in datasets is such an important problem to solve. Much of today’s work in artificial intelligence involves natural language processing, a field which studies the way language is used today and has been used in the past. The datasets we use to train artificially intelligent systems are usually collections of text that humans have written at some point in the past. If there are imbalances in how different groups of people tend to be described or represented in these datasets, then our machines will pick up on and potentially amplify those imbalances. Extreme manifestations of these biases like Tay, Microsoft’s 2016 chatbot infamous for tweeting racist and anti-Semitic statements after just a day of learning from anonymous posts on the Internet, magnify the importance of understanding the ways we use language. More recent examples include Amazon's AI tool for expediting hiring and recruiting, which was shut down after demonstrating extreme negative bias towards hiring candidates based on their gender.

Even when people do not mean to be malicious, their language can still exhibit biases that influence how our machines learn. For example, when history of science professor Londa Schiebinger attempted to Google Translate a Spanish article written about her, all of the pronouns became “he” and “him” rather than “she” and “her” simply because masculine pronouns were more common than feminine pronouns in the available data. In a later study, Schiebinger found more insidious translation errors that assumed genders for people of certain professions, based on the frequency of word usage in gendered languages such as German. The software engineers who made Google Translate probably did not mean for this to occur; they probably did not even account for that possibility as they were designing their translation algorithm. The moral of the story? To prevent these kinds of slip-ups, computer scientists need to consider the social impacts of their work at the beginning of their design process.

Identifying issues of bias and representation in datasets is a natural extension of many of the interesting ethical topics that we have talked about in CS106A so far this quarter. As we've mentioned before, our hope is that by introducing these sorts of topics early in computer science education, we can help the next generation of software developers and computer science researchers—which could include you!—be more mindful of the potential social implications of their work.

Assignment Overview

The rest of this handout will be broken into several sections. Each section defines a distinct, manageable milestone that will allow you to use the power of decomposition to build a complex final program out of many small, testable components.

1. Understand the dataset and word_data dictionary(overview and data processing): We have written all the code for this section. Peruse our filereading code and learn more about the dataset that you will be exploring and the nested data structure.
2. Get matplotlib installed (setup): Make sure that matplot lib has been installed so that you can use the functions in this module.
3. Creating the Plots (data visualization): Write some code to create bar charts for an inputted word.
4. Investigate bias in the dataset (ethics): Using your functional BiasBars application, explore the dataset to identify possible instances of biased/gendered use of language.

Milestone 1: Understand the dictionary structure

In this milestone you are going to understand the structure of the dictionary created by the provided file reading code.

This assignment uses real world data from RateMyProfessors.com, an online platform that enables students to leave anonymous, public reviews about their college/university professors. A typical review on RateMyProfessors.com consists of an overall numerical rating of quality (from 1-5), a number of qualitative tags (like "amazing lectures" or "difficult exams"), and a free-response comment section where students can write a short paragraph describing their experience with the professor. An example review for our very own Nick Parlante is shown below:

Figure 2. Example review from RateMyProfessors.com for Nick Parlante

The power of the Internet makes this platform for reviews accessible to the global community of students, empowering students to make decisions about classes they might want to take or universities they might want to attend based on the quality of instruction. The indirectness and anonymity of being behind a computer or phone screen also gives people a sense of security to say whatever they want, which can range from the supportive or constructive to the downright offensive or harmful. In analyzing this dataset you will be working to answer the following question: does a professor's gender influence the language people use to describe them?

To examine this question, we have collected and compiled a dataset of 20,000 reviews from RateMyProfessors.com posted over a 17-year span from 2001 to 2018. We have cleaned and organized the data into one large text file that will act as the source of information for the program you will write. There are 3 important components of every review that we have used to build the dataset: gender of the professor being reviewed, textual content of the free-response comment, and overall review quality (a numerical score from 1-5).

A note on gender vs sex:
In this dataset, gender is the only piece of information we have about these people’s social identities; the dataset does not include other salient identities such as race and ability. Furthermore, gender is only classified into the categories of woman and man, which means non-binary people are unfortunately not represented. We choose to describe the two genders included in this dataset as “woman” and “man” rather than “female” and “male,” as the former terms refer to gender and social role whereas the latter typically refer to sex assigned at birth. Professors do not have the opportunity to describe their own gender identity; this data represents the guesses of students. We will reflect further on this point in the ethics questions at the end of the assignment.

Loading in the data

We have written a few functions, namely convert_rating_to_index(rating), add_data_for_word(word(word_data, word, gender, rating), and read_file(filename) in biasbarsgraph.py. These functions are complete and you do not need to add code here. Feel free to peruse them.

The data structure we have built for you to use

To begin with, we need to consider the issue of being able to organize the data by the numerical rating associated with the review, since we want to be able to identify trends in how a given word is used in positive reviews vs. negative reviews. Since numerical rating is a float (real value) that can take on many different values between 1.0 and 5.0, we are going to make our data processing task simpler by representing review quality using only three "buckets":

• Reviews with a numerical rating of less than 2.5 will be considered "low reviews".
• Reviews with a numerical rating between 2.5 and 3.5 (inclusive on both ends of range) will be considered "medium reviews"
• Reviews with a numerical rating above 3.5 will be considered "high reviews"

With this knowledge, here is a data structure that we built for you to organize word frequencies (counts) across both gender and review quality. The data structure for this program (which we will refer to as word_data) is a dictionary that has a key for every word (a string) that we come across in the dataset. The value associated with each word is another (nested) dictionary, which maps gender to a list of word counts, broken down by rating quality (the ordering is counts for low reviews, then medium reviews, then high reviews). A partial example of this data structure would look something like this:

  
        {
            'great': {
                'W': [30, 100, 800],
                'M': [100, 200, 1500]
            },
            'teacher': {
                'W': [330, 170, 852],
                'M': [402, 250, 1194]
            }
        }

Let's break down the organization of this data structure a little bit with an example. Let's say we wanted to access the current count of occurrences of the word "great" in high reviews for women (which we can see to be 800 from the above diagram) . What steps could we take in our code to traverse the nested data structure to access that value?

• We can then access the overall word_data dictionary to get the data associated with the word "great" using the expression word_data["great"]. This gives us an inner dictionary that looks like this

  {
    'W': [30, 100, 800],
    'M': [100, 200, 1500]
  }
  

• Now, we must access the list of word counts associated with our gender of interest. To do that, we can use another level of dictionary access, now specifying the dictionary key for women, with the expression word_data["great"]["W"]. This expression gives us the following list

  [30, 100, 800]
  

• Finally, we're one step away from our end goal. The last step is to index into the innermost list to get the word count associated with the specific review bucket we want to analyze. We know that high reviews fall in the last bucket of our list (index 2), so we access our overall desired count with the expression word_data["great"]["W"][2] which finally gives us the desired count of 800.

Now that we have covered the structure as well as how to access different values within the nested structure, we can move on to the coding that you will have to do for the assignment: building a cool visualization for this data! We have already completed all of the file reading for you in the starter project.

Milestone 2: Getting matplotlib Installed

In this milestone you are going to make sure you have everything installed properly in order to complete the assignment.

Unzip and open this folder in pycharm as normal. Make sure you have the matplotlib package installed, which allows you to quickly and easily draw graphs. If you haven't done this already, open the Terminal in Pycharm and run the following command:

        python3 -m pip install matplotlib
      

If you have a Windows computer, substitute py for python3.

Milestone 3: Creating the Plots

In this section you will write code in plot_women_words(word_data, word, max_frequency) and plot_women_words(word_data, word, max_frequency) using your new matplotlib skills to create bar charts for each word. Each bar chart should have a separate bar for the low, medium, and high reviews measuring the frequency of the given word in that review category for that gender. Recall from Monday's lecture that in order to make a plot you need a list of x values and a list of y values. Below is example code to create a bar chart:

    
x_vals = # some list of x values
y_vals = # seom list of y values
plt.bar(x_vals, y_vals, color="tab:orange")
plt.title("Awesome Title Here")
    
  

For the bar chart for women's data, you will want to access the reviews in the dictionary with the key 'W' in order to build your list of y values. For the bar chart for men's data, you will want to access the reviews in the dictionary with the key 'M' in order to build your list of y values. The x values for each bar chart has been provided for you.

To test this, we have provided you with a main function that takes the word that you want to plot as a command line argument. To save you time, we wrote the main function, but feel free to check it out to understand how it works. When you are ready to test, you can run the following in the terminal:
py biasbarsgraph.py funny Use python3 instaed of py on Mac.
where you can replace 'funny' with any word that you want to see plotted. Note that the graphs may show up on top of each other so you can drag the top one over to the side in order to see the bottom graph.

Below are example screenshots for the plots of the word funny. These should pop up if you run the line from above.

Milestone 4: Identifying bias in the dataset and other interesting data science ethics questions

In this milestone you are going to think critically about the RateMyProfessors.com dataset and reflect on the relevant ethical issues that computer scientists should consider when working on data science problems.You will be answering the following questions in ethics.txt.

Diana Navas gave a fascinating lecture (or will give one soon depending on when you are reading this part of the handout) touching on ethics in the data that we look at. As Katie mentioned, many critical issues can arise when working with datasets that have real data that is based on data describing real people. In the last part of the assignment, we are going to ask you a few questions. Remember our moral from the earlier tale of Microsoft's Chatbot or Google's Translation software? That is, computer scientists need to consider the social impacts of their work at the beginning of their design process. We hope that these questions will provide a foundation for you to think critically about the social impacts of your work as you continue on into the wonderful field of computer science! As usual, please take the time to seriously think about each of the questions presented below and answer each in at least 2-3 well thought-out sentences.

1. Plot a few “neutral” words such as class, the, and teach. What do you see? Now plot a few more "loaded” words (thick normative terms) to investigate potential biases in the dataset. Possible examples: funny, rude, professor, teacher, mean, fair, unfair, genius, brilliant. What do you see? Include at least three “loaded” words and their corresponding frequencies for men and women for the high, medium, and low groups in your ethics.txt file.
2. Based on the definitions of fairness presented in class, under what conditions (that could be observed from a dataset like this) would it be unfair for a university to use either the ratings or the prevalence of particular words (such as brilliant or genius) as a factor in decisions to hire, tenure, or fire professors? Assume the universities are using end-of-term student evaluations written by their own students, which might have different distributions.
3. What kind of information do the subjects represented in a dataset (in this case, professors) deserve to know about the trends in the data? How could you as the programmer provide this information?
4. In class, we discussed problems of underrepresentation and “long tails” in data science. Although the data we gave you has not been edited other than to remove punctuation, our visualization focuses on average scores and commonly used words. What kinds of interactions between students and teachers, both positive and negative, might be uncommon or not well represented by this dataset but important? For example, how might underrepresented students’ positive experiences of underrepresented teachers appear or not appear in this dataset?
5. In this assignment, we asked you, “does a professor's gender influence the language people use to describe them?” That is an important question, and we haven’t given you the right kind of data to fully answer it: the dataset presents a binary classification of gender based on students’ beliefs as to the gender of the professor. There are some people in the dataset whose gender is misdescribed, and others, such as non-binary people, who do not have a category that fits them at all. If you could design the ratings website, how might you change or remove categories to address this problem?
6. In 106A, we create clearly-defined assignments for you to work on. We tell you what to do at each step and what counts as success at the end. In other words, we are formulating problems for you to solve. As we discussed in class, however, problem formulation is one of the ways in which your choices as a computer scientist embed values. Formulate a different problem related to the topics of professor evaluation or visualization of gendered patterns in language use, ideally one you could solve with your current skills. Explain why and for whom it would be good to solve that problem.

Once you've finished through thinking through and answering all these questions, make sure to stop and really admire the magnitude of what you've accomplished! You've wrangled a complex real-world dataset and used it to flex your ethical and critical thinking skills. Congrats on making it to the end!

Submitting your work

Once you've gotten all the parts of this assignment working, you're ready to submit!

Make sure to submit only the python files you modified for this assignment on Paperless. You should make sure to submit the files:

• biasbarsgraph.py
• ethics.txt

A Brief History of BiasBars

BiasBars was originally created as a spinoff of NameSurfer, a past CS106A assignment developed by Stanford lecturer Nick Parlante that asked students to graph data about the popularity of baby names over time. For an ethics-themed hackathon in April 2018, Jennie Yang and Monica Anuforo, two of our awesome section leaders in the CS198 program decided to create a parody of NameSurfer that instead graphed information about gender and RateMyProfessor reviews, based on an applet created by history professor Ben Schmidt of Northeastern University. (The data you will be using for this assignment will be very similar, but not from the exact same source as Professor Schmidt's applet.) Monica and Jennie worked with Colin Kincaid, a former CS198 Coordinator and CS106A Lecturer, to turn the NameSurfer spinoff developed at the hackathon into the original Bias Bars assignment, which was done in Java and assigned in the summer of 2018. The ethical themes explored by their assignment played really well with so many of the topics that we've covered in the course this quarter! So, Juliette Woodrow modified this BiasBars assignment to provide opportunities for exploring the interesting dataset using matplotlib, moving away from the original NameSurfer idea, but keeping the interesting ethical component.