Intro to AI Ethics/4. AI Fairness.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":1953634,"sourceType":"datasetVersion","datasetId":1154765}],"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"**This notebook is an exercise in the [AI Ethics](https://www.kaggle.com/learn/ai-ethics) course.  You can reference the tutorial at [this link](https://www.kaggle.com/alexisbcook/ai-fairness).**\n\n---\n","metadata":{}},{"cell_type":"markdown","source":"In the tutorial, you learned about different ways of measuring fairness of a machine learning model.  In this exercise, you'll train a few models to approve (or deny) credit card applications and analyze fairness.  Don't worry if you're new to coding: this exercise assumes no programming knowledge.\n\n# Introduction\n\nWe work with a **synthetic** dataset of information submitted by credit card applicants. \n\nTo load and preview the data, run the next code cell.  When the code finishes running, you should see a message saying the data was successfully loaded, along with a preview of the first five rows of the data.","metadata":{"papermill":{"duration":0.014709,"end_time":"2021-02-18T15:58:13.692777","exception":false,"start_time":"2021-02-18T15:58:13.678068","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Set up feedback system\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.ethics.ex4 import *\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\n# Load the data, separate features from target\ndata = pd.read_csv(\"../input/synthetic-credit-card-approval/synthetic_credit_card_approval.csv\")\nX = data.drop([\"Target\"], axis=1)\ny = data[\"Target\"]\n\n# Break into training and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)\n\n# Preview the data\nprint(\"Data successfully loaded!\\n\")\nX_train.head()","metadata":{"papermill":{"duration":1.439784,"end_time":"2021-02-18T15:58:15.144909","exception":false,"start_time":"2021-02-18T15:58:13.705125","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-27T08:31:35.924009Z","iopub.execute_input":"2024-02-27T08:31:35.924523Z","iopub.status.idle":"2024-02-27T08:31:40.139587Z","shell.execute_reply.started":"2024-02-27T08:31:35.924479Z","shell.execute_reply":"2024-02-27T08:31:40.137941Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Data successfully loaded!\n\n","output_type":"stream"},{"execution_count":1,"output_type":"execute_result","data":{"text/plain":"        Num_Children  Group  Income  Own_Car  Own_Housing\n288363             1      1   40690        0            1\n64982              2      0   75469        1            0\n227641             1      1   70497        1            1\n137672             1      1   61000        0            0\n12758              1      1   56666        1            1","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Num_Children</th>\n      <th>Group</th>\n      <th>Income</th>\n      <th>Own_Car</th>\n      <th>Own_Housing</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>288363</th>\n      <td>1</td>\n      <td>1</td>\n      <td>40690</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>64982</th>\n      <td>2</td>\n      <td>0</td>\n      <td>75469</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>227641</th>\n      <td>1</td>\n      <td>1</td>\n      <td>70497</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>137672</th>\n      <td>1</td>\n      <td>1</td>\n      <td>61000</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12758</th>\n      <td>1</td>\n      <td>1</td>\n      <td>56666</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":" The dataset contains, for each applicant:\n- income (in the `Income` column),\n- the number of children (in the `Num_Children` column),\n- whether the applicant owns a car (in the `Own_Car` column, the value is `1` if the applicant owns a car, and is else `0`), and\n- whether the applicant owns a home (in the `Own_Housing` column, the value is `1` if the applicant owns a home, and is else `0`)\n\nWhen evaluating fairness, we'll check how the model performs for users in different groups, as identified by the `Group` column: \n- The `Group` column breaks the users into two groups (where each group corresponds to either `0` or `1`).  \n- For instance, you can think of the column as breaking the users into two different races, ethnicities, or gender groupings.  If the column breaks users into different ethnicities, `0` could correspond to a non-Hispanic user, while `1` corresponds to a Hispanic user. \n\n\nRun the next code cell without changes to train a simple model to approve or deny individuals for a credit card.  The output shows the performance of the model.","metadata":{"papermill":{"duration":0.012323,"end_time":"2021-02-18T15:58:15.17055","exception":false,"start_time":"2021-02-18T15:58:15.158227","status":"completed"},"tags":[]}},{"cell_type":"code","source":"from sklearn import tree\nfrom sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\nimport matplotlib.pyplot as plt\n\n# Train a model and make predictions\nmodel_baseline = tree.DecisionTreeClassifier(random_state=0, max_depth=3)\nmodel_baseline.fit(X_train, y_train)\npreds_baseline = model_baseline.predict(X_test)\n\n# Function to plot confusion matrix\ndef plot_confusion_matrix(estimator, X, y_true, y_pred, display_labels=[\"Deny\", \"Approve\"],\n                          include_values=True, xticks_rotation='horizontal', values_format='',\n                          normalize=None, cmap=plt.cm.Blues):\n    cm = confusion_matrix(y_true, y_pred, normalize=normalize)\n    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=display_labels)\n    return cm, disp.plot(include_values=include_values, cmap=cmap, xticks_rotation=xticks_rotation,\n                     values_format=values_format)\n\n# Function to evaluate the fairness of the model\ndef get_stats(X, y, model, group_one, preds):\n        \n    y_zero, preds_zero, X_zero = y[group_one==False], preds[group_one==False], X[group_one==False]\n    y_one, preds_one, X_one = y[group_one], preds[group_one], X[group_one]\n    \n    print(\"Total approvals:\", preds.sum())\n    print(\"Group A:\", preds_zero.sum(), \"({}% of approvals)\".format(round(preds_zero.sum()/sum(preds)*100, 2)))\n    print(\"Group B:\", preds_one.sum(), \"({}% of approvals)\".format(round(preds_one.sum()/sum(preds)*100, 2)))\n    \n    print(\"\\nOverall accuracy: {}%\".format(round((preds==y).sum()/len(y)*100, 2)))\n    print(\"Group A: {}%\".format(round((preds_zero==y_zero).sum()/len(y_zero)*100, 2)))\n    print(\"Group B: {}%\".format(round((preds_one==y_one).sum()/len(y_one)*100, 2)))\n    \n    cm_zero, disp_zero = plot_confusion_matrix(model, X_zero, y_zero, preds_zero)\n    disp_zero.ax_.set_title(\"Group A\")\n    cm_one, disp_one = plot_confusion_matrix(model, X_one, y_one, preds_one)\n    disp_one.ax_.set_title(\"Group B\")\n    \n    print(\"\\nSensitivity / True positive rate:\")\n    print(\"Group A: {}%\".format(round(cm_zero[1,1] / cm_zero[1].sum()*100, 2)))\n    print(\"Group B: {}%\".format(round(cm_one[1,1] / cm_one[1].sum()*100, 2)))\n    \n# Evaluate the model    \nget_stats(X_test, y_test, model_baseline, X_test[\"Group\"]==1, preds_baseline)","metadata":{"papermill":{"duration":1.289337,"end_time":"2021-02-18T15:58:16.472414","exception":false,"start_time":"2021-02-18T15:58:15.183077","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-27T08:31:45.018877Z","iopub.execute_input":"2024-02-27T08:31:45.019317Z","iopub.status.idle":"2024-02-27T08:31:47.025483Z","shell.execute_reply.started":"2024-02-27T08:31:45.019286Z","shell.execute_reply":"2024-02-27T08:31:47.024169Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Total approvals: 38246\nGroup A: 8028 (20.99% of approvals)\nGroup B: 30218 (79.01% of approvals)\n\nOverall accuracy: 94.79%\nGroup A: 94.56%\nGroup B: 95.02%\n\nSensitivity / True positive rate:\nGroup A: 77.23%\nGroup B: 98.03%\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"The confusion matrices above show how the model performs on some test data. We also print additional information (calculated from the confusion matrices) to assess fairness of the model. For instance,\n- The model approved 38246 people for a credit card. Of these individuals, 8028 belonged to Group A, and 30218 belonged to Group B.\n- The model is 94.56% accurate for Group A, and 95.02% accurate for Group B. These percentages can be calculated directly from the confusion matrix; for instance, for Group A, the accuracy is (39723+7528)/(39723+500+2219+7528).\n- The true positive rate (TPR) for Group A is 77.23%, and the TPR for Group B is 98.03%. These percentages can be calculated directly from the confusion matrix; for instance, for Group A, the TPR is 7528/(7528+2219).\n\n# 1) Varieties of fairness\n\nConsider three different types of fairness covered in the tutorial:\n- **Demographic parity**: Which group has an unfair advantage, with more representation in the group of approved applicants?  (Roughly 50% of applicants are from Group A, and 50% of applicants are from Group B.)\n- **Equal accuracy**: Which group has an unfair advantage, where applicants are more likely to be correctly classified? \n- **Equal opportunity**:  Which group has an unfair advantage, with a higher true positive rate?","metadata":{"papermill":{"duration":0.014937,"end_time":"2021-02-18T15:58:16.502916","exception":false,"start_time":"2021-02-18T15:58:16.487979","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Check your answer (Run this code cell to get credit!)\nq_1.check()","metadata":{"papermill":{"duration":0.02343,"end_time":"2021-02-18T15:58:16.542486","exception":false,"start_time":"2021-02-18T15:58:16.519056","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-27T08:31:49.726664Z","iopub.execute_input":"2024-02-27T08:31:49.727859Z","iopub.status.idle":"2024-02-27T08:31:49.737242Z","shell.execute_reply.started":"2024-02-27T08:31:49.727809Z","shell.execute_reply":"2024-02-27T08:31:49.736203Z"},"trusted":true},"execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"1_VarietiesOfFairness\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: All of the fairness criteria are unfairly biased in favor of Group B. 79.01% of applicants are from Group B -- if demographic parity is our fairness criterion, fair would mean that 50% of the applicants are from Group B. The model is also slightly more accurate for applicants from Group B (with accuracy of 95.02%, vs 94.56% for Group A). The true positive rate is very high for Group B (98.03%, vs. 77.23% for Group A). In other words, for Group B, almost all people who should be approved are actually approved. For Group A, if you should be approved, your chances of actually being approved are much lower.","text/markdown":"<span style=\"color:#33cc33\">Solution:</span> All of the fairness criteria are unfairly biased in favor of Group B. 79.01% of applicants are from Group B -- if demographic parity is our fairness criterion, fair would mean that 50% of the applicants are from Group B. The model is also slightly more accurate for applicants from Group B (with accuracy of 95.02%, vs 94.56% for Group A). The true positive rate is very high for Group B (98.03%, vs. 77.23% for Group A). In other words, for Group B, almost all people who should be approved are actually approved. For Group A, if you should be approved, your chances of actually being approved are much lower."},"metadata":{}}]},{"cell_type":"markdown","source":"Run the next code cell without changes to visualize the model.","metadata":{"papermill":{"duration":0.015592,"end_time":"2021-02-18T15:58:16.573553","exception":false,"start_time":"2021-02-18T15:58:16.557961","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def visualize_model(model, feature_names, class_names=[\"Deny\", \"Approve\"], impurity=False):\n    plot_list = tree.plot_tree(model, feature_names=feature_names, class_names=class_names, impurity=impurity)\n    [process_plot_item(item) for item in plot_list]\n\ndef process_plot_item(item):\n    split_string = item.get_text().split(\"\\n\")\n    if split_string[0].startswith(\"samples\"):\n        item.set_text(split_string[-1])\n    else:\n        item.set_text(split_string[0])\n\nplt.figure(figsize=(20, 6))\nplot_list = visualize_model(model_baseline, feature_names=X_train.columns)","metadata":{"papermill":{"duration":0.466948,"end_time":"2021-02-18T15:58:17.055684","exception":false,"start_time":"2021-02-18T15:58:16.588736","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-27T08:31:51.099064Z","iopub.execute_input":"2024-02-27T08:31:51.099575Z","iopub.status.idle":"2024-02-27T08:31:51.887534Z","shell.execute_reply.started":"2024-02-27T08:31:51.099539Z","shell.execute_reply":"2024-02-27T08:31:51.886482Z"},"trusted":true},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"The flowchart shows how the model makes decisions:\n- `Group <= 0.5` checks what group the applicant belongs to: if the applicant belongs to Group A, then `Group <= 0.5` is true.\n- Entries like `Income <= 80210.5` check the applicant's income.\n\nTo follow the flow chart, we start at the top and trace a path depending on the details of the applicant.  If the condition is true at a split, then we move down and to the left branch.  If it is false, then we move to the right branch.\n\nFor instance, consider an applicant in Group B, who has an income of 75k.  Then, \n- We start at the top of the flow chart.  the applicant has an income of 75k, so `Income <= 80210.5` is true, and we move to the left.\n- Next, we check the income again. Since `Income <= 71909.5` is false, we move to the right.\n- The last thing to check is what group the applicant belongs to.  The applicant belongs to Group B, so `Group <= 0.5` is false, and we move to the right, where the model has decided to approve the applicant.\n\n# 2) Understand the baseline model\n\nBased on the visualization, how can you explain one source of unfairness in the model?\n\n**Hint**: Consider the example applicant, but change the group membership from Group B to Group A (leaving all other characteristics the same).  Is this slightly different applicant approved or denied by the model?","metadata":{"papermill":{"duration":0.016028,"end_time":"2021-02-18T15:58:17.088356","exception":false,"start_time":"2021-02-18T15:58:17.072328","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Check your answer (Run this code cell to get credit!)\nq_2.check()","metadata":{"papermill":{"duration":0.024547,"end_time":"2021-02-18T15:58:17.129966","exception":false,"start_time":"2021-02-18T15:58:17.105419","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-27T08:31:54.951783Z","iopub.execute_input":"2024-02-27T08:31:54.952252Z","iopub.status.idle":"2024-02-27T08:31:54.963608Z","shell.execute_reply.started":"2024-02-27T08:31:54.952211Z","shell.execute_reply":"2024-02-27T08:31:54.962111Z"},"trusted":true},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"2_BaselineModel\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: For all applicants with income between 71909.5 and 88440.5, the model's decision depends on group membership: the model approves applicants from Group B and denies applicants from Group A.  Since we want the model to treat individuals from Group A and Group B fairly, this is clearly a bad model.  Although this data is very simple, in practice, with much larger data, visualizing the how a model makes decisions can be very useful to more deeply understand what might be going wrong with a model.","text/markdown":"<span style=\"color:#33cc33\">Solution:</span> For all applicants with income between 71909.5 and 88440.5, the model's decision depends on group membership: the model approves applicants from Group B and denies applicants from Group A.  Since we want the model to treat individuals from Group A and Group B fairly, this is clearly a bad model.  Although this data is very simple, in practice, with much larger data, visualizing the how a model makes decisions can be very useful to more deeply understand what might be going wrong with a model."},"metadata":{}}]},{"cell_type":"markdown","source":"Next, you decide to remove group membership from the training data and train a new model.  Do you think this will make the model treat the groups more equally?\n\nRun the next code cell to see how this new **group unaware** model performs.","metadata":{"papermill":{"duration":0.016484,"end_time":"2021-02-18T15:58:17.163351","exception":false,"start_time":"2021-02-18T15:58:17.146867","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Create new dataset with gender removed\nX_train_unaware = X_train.drop([\"Group\"],axis=1)\nX_test_unaware = X_test.drop([\"Group\"],axis=1)\n\n# Train new model on new dataset\nmodel_unaware = tree.DecisionTreeClassifier(random_state=0, max_depth=3)\nmodel_unaware.fit(X_train_unaware, y_train)\n\n# Evaluate the model\npreds_unaware = model_unaware.predict(X_test_unaware)\nget_stats(X_test_unaware, y_test, model_unaware, X_test[\"Group\"]==1, preds_unaware)","metadata":{"papermill":{"duration":1.048652,"end_time":"2021-02-18T15:58:18.228696","exception":false,"start_time":"2021-02-18T15:58:17.180044","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-27T08:31:56.279976Z","iopub.execute_input":"2024-02-27T08:31:56.280450Z","iopub.status.idle":"2024-02-27T08:31:57.780494Z","shell.execute_reply.started":"2024-02-27T08:31:56.280402Z","shell.execute_reply":"2024-02-27T08:31:57.779137Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"Total approvals: 36670\nGroup A: 11624 (31.7% of approvals)\nGroup B: 25046 (68.3% of approvals)\n\nOverall accuracy: 92.66%\nGroup A: 93.61%\nGroup B: 91.72%\n\nSensitivity / True positive rate:\nGroup A: 93.24%\nGroup B: 86.21%\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3) Varieties of fairness, part 2\n\nHow does this model compare to the first model you trained, when you consider **demographic parity**, **equal accuracy**, and **equal opportunity**?  Once you have an answer, run the next code cell.","metadata":{"papermill":{"duration":0.018716,"end_time":"2021-02-18T15:58:18.266646","exception":false,"start_time":"2021-02-18T15:58:18.24793","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Check your answer (Run this code cell to get credit!)\nq_3.check()","metadata":{"papermill":{"duration":0.026228,"end_time":"2021-02-18T15:58:18.311867","exception":false,"start_time":"2021-02-18T15:58:18.285639","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-27T08:32:00.992997Z","iopub.execute_input":"2024-02-27T08:32:00.993495Z","iopub.status.idle":"2024-02-27T08:32:01.004239Z","shell.execute_reply.started":"2024-02-27T08:32:00.993459Z","shell.execute_reply":"2024-02-27T08:32:01.003075Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"3_VarietiesOfFairnessTwo\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: When we consider demographic parity, the new model is still biased in favor of Group B, but is now a bit more fair than the original model. But now, if you consider either equal accuracy or equal opportunity, the model is biased in favor of Group A! It's also important to note that the overall accuracy of the model has dropped -- for each group, the model is making slightly less accurate decisions.","text/markdown":"<span style=\"color:#33cc33\">Solution:</span> When we consider demographic parity, the new model is still biased in favor of Group B, but is now a bit more fair than the original model. But now, if you consider either equal accuracy or equal opportunity, the model is biased in favor of Group A! It's also important to note that the overall accuracy of the model has dropped -- for each group, the model is making slightly less accurate decisions."},"metadata":{}}]},{"cell_type":"markdown","source":"You decide to train a third potential model, this time with the goal of having each group have even representation in the group of approved applicants.  (This is an implementation of group thresholds, which you can optionally read more about [here](https://pair-code.github.io/what-if-tool/ai-fairness.html).)  \n\nRun the next code cell without changes to evaluate this new model.  ","metadata":{"papermill":{"duration":0.018855,"end_time":"2021-02-18T15:58:18.34974","exception":false,"start_time":"2021-02-18T15:58:18.330885","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Change the value of zero_threshold to hit the objective\nzero_threshold = 0.11\none_threshold = 0.99\n\n# Evaluate the model\ntest_probs = model_unaware.predict_proba(X_test_unaware)[:,1]\npreds_approval = (((test_probs>zero_threshold)*1)*[X_test[\"Group\"]==0] + ((test_probs>one_threshold)*1)*[X_test[\"Group\"]==1])[0]\nget_stats(X_test, y_test, model_unaware, X_test[\"Group\"]==1, preds_approval)","metadata":{"papermill":{"duration":0.691332,"end_time":"2021-02-18T15:58:19.06026","exception":false,"start_time":"2021-02-18T15:58:18.368928","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-27T08:32:02.970292Z","iopub.execute_input":"2024-02-27T08:32:02.970816Z","iopub.status.idle":"2024-02-27T08:32:03.683845Z","shell.execute_reply.started":"2024-02-27T08:32:02.970777Z","shell.execute_reply":"2024-02-27T08:32:03.682486Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"Total approvals: 38241\nGroup A: 19869 (51.96% of approvals)\nGroup B: 18372 (48.04% of approvals)\n\nOverall accuracy: 79.38%\nGroup A: 79.74%\nGroup B: 79.02%\n\nSensitivity / True positive rate:\nGroup A: 100.0%\nGroup B: 63.64%\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"# 4) Varieties of fairness, part 3\n\nHow does this final model compare to the previous models, when you consider **demographic parity**, **equal accuracy**, and **equal opportunity**?","metadata":{"papermill":{"duration":0.021832,"end_time":"2021-02-18T15:58:19.103892","exception":false,"start_time":"2021-02-18T15:58:19.08206","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Check your answer (Run this code cell to get credit!)\nq_4.check()","metadata":{"papermill":{"duration":0.029371,"end_time":"2021-02-18T15:58:19.1549","exception":false,"start_time":"2021-02-18T15:58:19.125529","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-02-27T08:32:07.660434Z","iopub.execute_input":"2024-02-27T08:32:07.661728Z","iopub.status.idle":"2024-02-27T08:32:07.673222Z","shell.execute_reply.started":"2024-02-27T08:32:07.661681Z","shell.execute_reply":"2024-02-27T08:32:07.671983Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.Javascript object>","application/javascript":"parent.postMessage({\"jupyterEvent\": \"custom.exercise_interaction\", \"data\": {\"outcomeType\": 1, \"valueTowardsCompletion\": 0.25, \"interactionType\": 1, \"questionType\": 4, \"questionId\": \"4_VarietiesOfFairnessThree\", \"learnToolsVersion\": \"0.3.4\", \"failureMessage\": \"\", \"exceptionClass\": \"\", \"trace\": \"\"}}, \"*\")"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Solution: This model acheives nearly equal representation in the pool of approved applicants from each group -- if demographic parity is what we care about, then this model is much more fair than the first two models. Accuracy is roughly the same for each group, but there is a substantial drop in overall accuracy for each group. If we examine the model for equal opportunity fairness, the model is biased in favor of Group A: all individuals from Group A who should be approved are approved, whereas only 63% of individuals from Group B who should be approved are approved.  (This is similar to the dynamic in the first model, with the favored group switched -- that is, in the first model, nearly all individuals from Group B who should be approved were approved by the model.","text/markdown":"<span style=\"color:#33cc33\">Solution:</span> This model acheives nearly equal representation in the pool of approved applicants from each group -- if demographic parity is what we care about, then this model is much more fair than the first two models. Accuracy is roughly the same for each group, but there is a substantial drop in overall accuracy for each group. If we examine the model for equal opportunity fairness, the model is biased in favor of Group A: all individuals from Group A who should be approved are approved, whereas only 63% of individuals from Group B who should be approved are approved.  (This is similar to the dynamic in the first model, with the favored group switched -- that is, in the first model, nearly all individuals from Group B who should be approved were approved by the model."},"metadata":{}}]},{"cell_type":"markdown","source":"This is only a short exercise to explore different types of fairness, and to illustrate the tradeoff that can occur when you optimize for one type of fairness over another.  We have focused on model training here, but in practice, to really mitigate bias, or to make ML systems fair, we need to take a close look at every step in the process, from data collection to releasing a final product to users. \n\nFor instance, if you take a close look at the data, you'll notice that on average, individuals from Group B tend to have higher income than individuals from Group A, and are also more likely to own a home or a car.  Knowing this will prove invaluable to deciding what fairness criterion you should use, and to inform ways to achieve fairness.  (*For instance, it would likely be a bad aproach, if you did not remove the historical bias in the data and then train the model to get equal accuracy for each group.*)\n\nIn this course, we intentionally avoid taking an opinionated stance on how exactly to minimize bias and ensure fairness in specific projects.  This is because the correct answers continue to evolve, since AI fairness is an active area of research.  This lesson was a hands-on introduction to the topic, and you can continue your learning by reading blog posts from the [Partnership on AI](https://www.partnershiponai.org/research-lander/) or by following conferences like the [ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)](https://facctconference.org/).","metadata":{"papermill":{"duration":0.021224,"end_time":"2021-02-18T15:58:19.19787","exception":false,"start_time":"2021-02-18T15:58:19.176646","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"# Keep going\n\nContinue to **[learn how to use model cards](https://www.kaggle.com/var0101/model-cards)** to make machine learning models transparent to large audiences.","metadata":{"papermill":{"duration":0.021672,"end_time":"2021-02-18T15:58:19.241234","exception":false,"start_time":"2021-02-18T15:58:19.219562","status":"completed"},"tags":[]}}]}