CS6603 Final Project Solution

1. Must have at least a sample size of 500 observations
2. Must have at least two variables belonging to a legally recognized protected class
3. Must have at least two dependent variables (outcome variables) that could result in favorable or unfavorable outcomes [Note: Use your subjective opinion based on the discussions we’ve had in class]
4. Must be related to one of the regulated domains Credit, Education, Employment, or Housing and ‘Public Accommodation’ [Note: Loosely, any dataset that could have potential bias in outcomes based on protected class membership is acceptable. Also, don’t be biased by how the dataset is labeled/organized – you can think creatively about how to structure the dataset so it’s compliant to the requirements]

Answer the following questions in the final project report:
• Which dataset did you select?
• Which regulated domain does your dataset belong to?
• How many observations are in the dataset?
• How many variables in the dataset?
• Which variables did you select as your dependent variables?
• How many and which variables in the dataset are associated with a legally recognized protected class? Which legal precedence/law (as discussed in the lectures) does each protected class fall under?

Step 2:
1) Identify the members associated with your protected class variables and group together into a subset of membership categories as appropriate
2) Discretize the values associated with your dependent variables into discrete categories/numerical values as appropriate
3) Compute the frequency of each membership category associated with each of your protected class variables from Step 2.1
4) Create a histogram for each protected class variable that graphs the frequency values of its membership categories as a function of the dependent variables


Provide the following in the final project report:
• Table documenting the relationship between members and membership categories for each protected class variable (from Step 2.1)
• Table documenting the relationship between values and discrete categories/numerical values associated with your dependent variables (from Step 2.2)
• Table providing the computed frequency values for the membership categories each protected class variable
(from Step 2.3)
• Histograms derived from Step 2.4


Step 3: For the next set of questions, you are allowed to code up your own mathematical formulations, modify open-source code that wasn’t developed for this course, or modify code found from the AI Fairness 360 Open Source Toolkit (https://aif360.mybluemix.net/) or the What-If Tool (https://pair-code.github.io/what-if-tool/) to work with your dataset. Note: Others have found it easier to create their own formulas based on the fairness definitions found in the class lectures or on the toolkit website rather than modifying the code in the AI Fairness or What-If Tool packages. If you chose not to use or gain working-knowledge in using Python throughout this course, using these packages is not advised.

1) Based on your dataset, identify the privileged/unprivileged groups associated with each of your protected class variables
3) Select a pre-processing bias mitigation algorithm to transform the original dataset (e.g. Reweighting, Disparate Impact Remover, etc.) as a function of one of your dependent variables
4) Use the two fairness metrics identified in 3.2 and compute the fairness metrics on the transformed dataset

Provide the following in the final project report:
• Provide the resulting code (can be as an additional .ipynb file if submitting a PDF)
• Provide a table documenting the protected class variable selected, the privileged/unprivileged groups/values, the pre-processing bias mitigation function selected, and the fairness metrics/resulting values computed in Step 3.2 and Step 3.4


Step 4: There are two options for Step 4 – Choose one to complete for the final project.

Option A: For the next set of questions, you are allowed to code up your own algorithm, modify open-source code that wasn’t developed for this course, or modify code found from the AI Fairness 360 Open Source Toolkit to work with your dataset (https://github.com/IBM/AIF360/tree/master/examples). For example, code for training a classifier based on a credit scoring example can be found here:
https://github.com/IBM/AIF360/blob/master/examples/demo_reweighing_preproc.ipynb. Note: Others have found it easier to create their own algorithm rather than modifying the code in the AI Fairness package. If you chose not to use or gain working-knowledge in using Python throughout this course, using this package is not advised.
1) Randomly split your original dataset into training and testing datasets
2) Randomly split your transformed dataset into training and testing datasets (from Step 3.3)
3) Train a classifier using the original training dataset from Step 4.1; select one of your dependent variables as the output label to train your classifier.
4) Train a classifier using the transformed training dataset from Step 4.2; select one of your dependent variables as the output label to train your classifier.
5) Select the privileged/unprivileged groups associated with one of your protected class variables (from Step 3.1); Use the two fairness metrics identified in Step 3.2 and and compute the fairness metrics for the classifier output associated with the original testing dataset and the transformed testing dataset
6) For each fairness metric, in table format, indicate if there were any differences in the outcomes for the privileged versus unprivileged group. Was there a positive change, negative change, or no change on that fairness metric after transforming the dataset (from Step 3.4)? Was there a positive change, negative change, or no change on that fairness metric after training the classifier - with respect to the original testing dataset and the transformed testing dataset? [Note: Use your subjective opinion]

Provide the following in the final project report:
• Provide the resulting code (can be as an additional .ipynb file if submitting a PDF)
• Document 1) the privileged/unprivileged groups, 2) the dependent variable, 3) the quantitative results from applying the two fairness metrics on the classifier output associated with the original and transformed dataset, 4) a table documenting whether there was positive, negative, or no change in each of the fairness metrics after transforming the dataset, after training the classifier on the original dataset, and after training the classifier on the transformed dataset.

Option B: For the next set of questions, you are to design your own bias mitigation algorithm
1) Design your own bias mitigation algorithm (must be different than ones already represented in the aif360.algorithms.preprocessing class) to transform your original dataset [Note: Provide sufficient comments in your code so that the algorithm/math can be deciphered]
2) Randomly split your original dataset into training and testing datasets
3) Apply your bias mitigation algorithm to your original testing dataset in order to produce a transformed testing dataset; select one of your protected class variables and one of your dependent variables as the output label
4) Randomly split your transformed dataset into training and testing datasets
5) Select the privileged/unprivileged groups associated with your protected class variables; Use the two fairness metrics identified in Step 3.2 and compute the fairness metrics on the original testing dataset and the transformed testing dataset
6) For each fairness metric, in table format, discuss if there were any differences in the outcomes for the privileged versus unprivileged group. Was there a positive change, negative change, or no change on that fairness metric after transforming the dataset earlier in Step 3.4? Was there a positive change, negative change, or no change on that fairness metric after applying your bias mitigation algorithm - with respect to the original testing dataset and the transformed testing dataset? [Note: Use your subjective opinion]


Provide the following in the final project report:
• Provide the resulting code (can be as an additional .ipynb file if submitting a PDF)
• Document 1) the privileged/unprivileged groups, 2) the dependent variable, 3) the quantitative results from applying the two fairness metrics associated with the original testing dataset and transformed testing dataset after bias mitigation, 4) a table documenting whether there was positive, negative, or no change in each of the fairness metrics on the transformed dataset from Step 3.4, the original testing dataset (Step 4.5), and on the transformed dataset after applying your bias mitigation algorithm (Step 4.5)


Step 5:
• If you are an individual (team of 1), Provide the following in the final project report:
o Step 5: I am a team of one
• If you are a team > 1, Provide the following in the final project report:
o List the members of your project team
o Graph the results from applying the two fairness metrics on your privileged/unprivileged groups as derived from Step 3.2, 3.4, and 4.5
o Explain which fairness metric (if any) is best and provide a justification for your answer
o Each team member must provide a separate answer to the following questions in no-more than a one-paragraph response (this is to be included in the submitted group report, with a reference to the student author). Note: If a group member fails to provide a response, the team is free to indicate that in the final report submission with No Response and a reference to the student author.
 Did any of these approaches seems to work to mitigate bias (or increase fairness)? Explain your reasoning. Did any group receive a positive advantage? Was any group disadvantaged by these approaches? What issues would arise if you used these methods to mitigate bias?


Step 6: - Turn in a report plus the code associated with the final project. If you are working on the project as a team, only one person needs to submit this assignment. Please note that, when submitting a jupyter notebook (.ipynb) for the assignment submission, you need to make sure you have clearly printed/displayed all outputs necessary for receiving credit (as you would for a PDF submission) before submitting. This means that the jupyter notebooks must be run before your submission. Credit would not be awarded for just submitting the code in the notebook and not displaying the output. Note that you can still submit the final report as a PDF in addition to the jupyter notebook.