Description
TASK (20 points)
You are a new hire in a mid-size company. You have just completed your third task and handed in
your report. Your boss is meeting with officials from Chirper Inc. You eagerly wait near the coffee
machine opposite to the boss’s office waiting to be called in.
You were not wrong. After the lunch,
you were approached by your technical manager. “Our boss wants to see us – it’s urgent. Let’s go!”,
he commanded. Following your manager, you enter the meeting room. The CEO and the senior
developer already wait inside.
Your CEO said: “Great job! The director of the hospital and the infectious diseases researchers
were impressed by the results your model generated and are planning to deploy it on their systems
to identify patients resistant to the more viral strain of the virus.
The interpretable linear model you designed for predicting used car prices has brought in several
calls for collaboration. Chirper Inc., the social media giant, intends to analyze happiness among
constituents in these testing times. They want to send out support messages to constituents in areas
generating chirps with low average happiness.
They want our help to build a linear model that can
fairly predict these happiness scores and identify such areas. Egregious human rights violations
involving race in the past year have rocked our nation. Keep in mind that fairness of the model is
critical and unlike several modern AI systems our predictions should not be biased. Chirper Inc.
does not want to be audited by the Federal Government and fined for violating federal antidiscrimination statutes. Hence, the predictions should not reveal sensitive info about ethnicity
You have two weeks. I want a detailed report describing your main findings, on my desk, on
Thursday, March 25, at 10 am.”
Hint: This task is related to fairness and bias. You have to design a linear model that can fairly predict
happiness and identify areas with average happiness < 5.8. Happiness also known as “valence” has been
measured for a select few counties in the nation. Our model will help Chirper in predicting valence and the
impact of socio-demographic features on it. In this study, mean square error is not the primary focus.
Instead, we must focus on building a fair model. Because the primary focus is the measure of fairness, we
would like you to build three models – race aware, race blind and fair.
Your Technical Manager said: “I have shared the data file (chirper-happiness.csv) with you. It
consists of:
• Id2 which gives the Region Id,
• totalGroup1 is the number of people in ethnic group 1,
• totalGroup2 is the number of people in ethnic group 2,
• percent_bachelorPlus is the percentage of population with at least a bachelor’s degree
• households_meanIncome gives us the mean income per household in the region and
• meanvalence gives us the mean happiness in the region.
Correlation between the predicted outcome and independent predictors can be used as a proxy for
measuring fairness. Our goal is to ensure that valence predictions are not biased towards one of
the ethnic group variables. We define protected variables as ones along which biases can be
observed in the outcome. In our case, these are – totalGroup1 and (or) totalGroup2. Your analysis
report should consist of the following four models.”
1. Ground Truth Model – Generate correlations and scatterplots of ground truth valence
with respect to percent_bachelorPlus and households_meanIncome with each of the
scatterplots conditioned on totalGroup1 and totalGroup2. From the scatterplots state
whether you see a bias to one ethnic group? Describe why you think so.
The prediction models are considered fair if the correlations and scatter plot of predicted outcome
vs independent variables conditioned on ethnic group variables are similar to that of ground truth
model.
2. Ethnic Group Aware Model (Prediction Model I) – Build a linear regression model using
all predictors, on a percentage of the data provided. Predict the valence on the entire dataset.
Analyze fairness of predicted valence by comparing the correlations and scatterplots of
Group Aware Model with Ground Truth Model. Report your observations on the
comparison and state if there is bias to one of the two groups when the predicted valence
threshold is set to 5.8.
3. Ethnic Group Blind Model (Prediction Model II) – Build a linear regression model
without using totalGroup1 and totalGroup2 as predictors, on a percentage of the data
provided. Predict the valence on the entire dataset. Analyze fairness of predicted valence
by comparing the correlations and scatterplots of Group Blind Model with Ground Truth
Model.
Report your observations on the comparison. Does hiding these protected variables
change the bias identified in the previous model change here? Describe either way.
4. Fair Model – Our R&D team has published a paper on debiasing linear models (available
at: https://arxiv.org/pdf/1910.12854.pdf). On the basis of this work, we have written two
helper functions that you have to use in order to debias the outcome variable. The python
file consisting of these helper functions are available in fair_var.py.
Hint: If you run into doubts refer the Fairness lecture and slides from Week 7. Additionally, you
can attend office hours or post questions on Piazza.
The Senior Developer took you aside and said: “My task will be to maintain your code.
However, remember that I’m not a Data Scientist like you – so you have to be very careful when
you are writing your code. Write comments and try to explain any nontrivial section.”
Data
The dataset to be used is chirper-happiness.csv and helper functions for debiasing the outcome
variable are in fair_var.py.
Report
To help you, I prepared a template. See https://www.overleaf.com/read/vnvhqxkpdhbk.
You are encouraged to use the template, but you are free to use other editors or make modifications.
Just ensure that the final submission of the report has to be in PDF. Submission of report has to be
done on Blackboard. As always submit your code on GitHub classroom and report on Blackboard.
Code Submission
We have created a GitHub Classroom where you can create private repositories. We will update
the class on Piazza on how to go about uploading your solutions on this platform.
Grading Rules
In order to grade your work, we will role-play the following situation. We will assume, that you
are a new-hire in our company. You are asked to provide a comprehensive technical report that
illustrates your findings. We will evaluate it from the perspective of three people.
• Your CEO (she would like to hear high level stuff. She will probably only read the
conclusions and look at main figure). (4 points for report).
• Your manager (he would like to see a detailed report; he might also look at some parts of
the code). (6 points for report and 2 points for code).
• A senior developer (they would like to see the code and won’t read the report at all). (8
points for code).
Your final score is: 10 points for report and 10 points for code.
Don’t Panic
Don’t panic. We understand that this is a large, open ended task. We also understand that this
might be the very first technical report that you were asked to write. We are dedicated to help you
do your best work all while keeping the standards high. We acknowledge that you have limited
time and resources to complete the task. This report doesn’t have to be perfect for 100% score. If
something is not clear, ask your questions on Piazza or reach out to us during office hours.
Note: Cite any source you use (even if you adopt/copy a snippet of code). Failure to do so
would amount to plagiarism.
