# Fairness

Artificial Intelligence is great but it can be used incorrectly. Machine Learning, the most widely used AI techniques, relies heavily on data. It is a common misconception that AI is absolutely objective, since AI is objective only in the sense of learning what human teaches. The data provided by human can be highly-biased. It has been found in 2016 that COMPAS, the algorithm used for recidivism prediction produces much higher false positive rate for black people than white people. XING, a job platform similar to Linked-in, was found to rank less qualified male candidates higher than more qualified female candidates. Publicly available commercial face recognition online services provided by Microsoft, Face++, and IBM respectively are found to suffer from achieving much lower accuracy on females with darker skin color. Bias in ML has been almost ubiquitous when the application is involved in people and it has already hurt the benefit of people in minority groups or historically disadvantageous groups. Not only people in minority groups but everyone should care about the bias in AI. If no one cares, it is highly likely that the next person who suffers from biased treatment is one of us.

Google has a a great little video explaining bias and fairness in plain terms.

Below is a slightly more technical exploration of fairness using Tensorflow and the adult census dataset.

## The dataset

The Adult Census Income dataset is commonly used in machine learning literature. This data was extracted from the 1994 Census bureau database by Ronny Kohavi and Barry Becker.

Each example in the dataset contains the following demographic data for a set of individuals who took part in the 1994 Census:

### Numeric Features

• capital_gain: Capital gain made by the individual, represented in US Dollars.
• hours_per_week: Hours worked per week.
• fnlwgt: The number of individuals the Census Organizations believes that set of observations represents.
• education_num: An enumeration of the categorical representation of education. The higher the number, the higher the education that individual achieved. For example, an education_num of 11 represents Assoc_voc (associate degree at a vocational school), an education_num of 13 represents Bachelors, and an education_num of 9 represents HS-grad (high school graduate).
• age: The age of the individual in years.
• capital_loss: Capital loss mabe by the individual, represented in US Dollars.

### Categorical Features

• gender: Gender of the individual available only in binary choices: Female or Male.
• education: The highest level of education achieved for that individual.
• occupation: The occupation of the individual. Example include: tech-support, Craft-repair, Other-service, Sales, Exec-managerial and more.
• relationship: The relationship of each individual in a household. Examples include: Wife, Own-child, Husband, Not-in-family, Other-relative, and Unmarried.
• workclass: The individual’s type of employer. Examples include: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, and Never-worked.
• marital_status: Marital status of the individual. Examples include: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, and Married-AF-spouse.
• race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Black, and Other.
• native_country: Country of origin of the individual. Examples include: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, and more.

The prediction task is to determine whether a person makes over $50,000 US Dollar a year. ### Label • income_bracket: Whether the person makes more than$50,000 US Dollars annually.

All the examples extracted for this dataset meet the following conditions:
* age is 16 years or older.
* The adjusted gross income (used to calculate income_bracket) is greater than $100 USD annually. * fnlwgt is greater than 0. * hours_per_week is greater than 0. ## Setup Let’s import some modules that will be used throughout the notebook. import os import numpy as np import matplotlib.pyplot as plt import pandas as pd import tensorflow as tf import tempfile !pip install seaborn==0.8.1 import seaborn as sns import itertools from sklearn.metrics import confusion_matrix from sklearn.metrics import roc_curve, roc_auc_score from sklearn.metrics import precision_recall_curve from google.colab import widgets # For facets from IPython.core.display import display, HTML import base64 !pip install -q hopsfacets import hopsfacets as facets from hopsfacets.feature_statistics_generator import FeatureStatisticsGenerator print('Modules have been imported.')  With the modules now imported, we can load the Adult dataset into a pandas DataFrame data structure. COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num", "marital_status", "occupation", "relationship", "race", "gender", "capital_gain", "capital_loss", "hours_per_week", "native_country", "income_bracket"] train_df = pd.read_csv( "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data", names=COLUMNS, sep=r'\s*,\s*', engine='python', na_values="?") test_df = pd.read_csv( "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test", names=COLUMNS, sep=r'\s*,\s*', skiprows=[0], engine='python', na_values="?") # Drop rows with missing values train_df = train_df.dropna(how="any", axis=0) test_df = test_df.dropna(how="any", axis=0) print('UCI Adult Census Income dataset loaded.')  UCI Adult Census Income dataset loaded.  ## Analyzing the Adult Dataset with Facets Exploratory data analysis (EDA) is used to understand, summarize and analyse the contents of a dataset, usually to investigate a specific question or to prepare for more advanced modeling. It’is important to understand your dataset before diving straight into the prediction task. Some important questions to investigate when auditing a dataset for fairness: • Are there missing feature values for a large number of observations? • Are there features that are missing that might affect other features? • Are there any unexpected feature values? • What signs of data skew do you see? Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive. To start, we can use Facets Overview to quickly analyze the distribution of values across the Adult dataset. #@title Visualize the Data in Facets fsg = FeatureStatisticsGenerator() dataframes = [ {'table': train_df, 'name': 'trainData'}] censusProto = fsg.ProtoFromDataFrames(dataframes) protostr = base64.b64encode(censusProto.SerializeToString()).decode("utf-8") HTML_TEMPLATE = """<link rel="import" href="https://raw.githubusercontent.com/PAIR-code/facets/master/facets-dist/facets-jupyter.html"> <facets-overview id="elem"></facets-overview> <script> document.querySelector("#elem").protoInput = "{protostr}"; </script>""" html = HTML_TEMPLATE.format(protostr=protostr) display(HTML(html))  Questions you should ask yourself when exploring the data: 1. Are there missing feature values for a large number of observations? 2. Are there features that are missing that might affect other features? 3. Are there any unexpected feature values? 4. What signs of data skew do you see? We can see from reviewing the missing columns for both numeric and categorical features that there are no missing feature values, so that is not a concern here. By looking at the min/max values and histograms for each numeric feature, we can pinpoint any extreme outliers in our data set. For hours_per_week, we can see that the minimum is 1, which might be a bit surprising, given that most jobs typically require multiple hours of work per week. For capital_gain and capital_loss, we can see that over 90% of values are 0. Given that capital gains/losses are only registered by individuals who make investments, it’s certainly plausible that less than 10% of examples would have nonzero values for these feature, but we may want to take a closer look to verify the values for these features are valid. In looking at the histogram for gender, we see that over two-thirds (approximately 67%) of examples represent males. This strongly suggests data skew, as we would expect the breakdown between genders to be closer to 50/50. ### A Deeper Dive To futher explore the dataset, we can use Facets Dive, a tool that provides an interactive interface where each individual item in the visualization represents a data point. But to use Facets Dive, we need to convert our data to a JSON array. Thankfully the DataFrame method to_json() takes care of this for us. Run the cell below to perform the data transform to JSON and also load Facets Dive. #@title Set the Number of Data Points to Visualize in Facets Dive SAMPLE_SIZE = 2500 #@param train_dive = train_df.sample(SAMPLE_SIZE).to_json(orient='records') HTML_TEMPLATE = """<link rel="import" href="https://raw.githubusercontent.com/PAIR-code/facets/master/facets-dist/facets-jupyter.html"> <facets-dive id="elem" height="600"></facets-dive> <script> var data = {jsonstr}; document.querySelector("#elem").data = data; </script>""" html = HTML_TEMPLATE.format(jsonstr=train_dive) display(HTML(html))  Use the menus on the left panel of the visualization to change how the data is organized: 1. In the Faceting | X-Axis menu, select education, and in the Display | Color and Display | Type menus, select income_bracket. 2. Next, in the Faceting | X-Axis menu, select marital_status, and in the Display | Color and Display | Type menus, select gender. Some fairness questions to ask using Facets: • What’s missing? • What’s being overgeneralized? • What’s being underrepresented? • How do the variables, and their values, reflect the real world? • What might we be leaving out? Higher education levels generally tend to correlate with a higher income bracket. An income level of greater than$50,000 is more heavily represented in examples where education level is Bachelor’s degree or higher. In most marital-status categories, the distribution of male vs. female values is close to 1:1. The one notable exception is “married-civ-spouse”, where male outnumbers female by more than 5:1. Given that we already discovered in Task #1 that there is a disproportionately high representation of men in our data set, we can now infer that it’s married women specifically that are underrepresented in our data.

The better you know what’s going on in your data, the more insight you’ll have as to where unfairness might creep in!

feature = 'capital_gain / capital_loss' #@param ["", "hours_per_week", "fnlwgt", "gender", "capital_gain / capital_loss", "age"] {allow-input: false}

if feature == "hours_per_week":
print(
'''It does seem a little strange to see 'hours_per_week' max out at 99 hours,
which could lead to data misrepresentation. One way to address this is by
representing 'hours_per_week' as a binary "working 40 hours/not working 40
hours" feature. Also keep in mind that data was extracted based on work hours
being greater than 0. In other words, this feature representation exclude a
subpopulation of the US that is not working. This could skew the outcomes of the
model.''')
if feature == "fnlwgt":
print(
"""'fnlwgt' represents the weight of the observations. After fitting the model
to this data set, if certain group of individuals end up performing poorly
compared to other groups, then we could explore ways of reweighting each data
point using this feature.""")
if feature == "gender":
print(
"""Looking at the ratio between men and women shows how disproportionate the data
is compared to the real world where the ratio (at least in the US) is closer to
1:1. This could pose a huge probem in performance across gender. Considerable
measures may need to be taken to upsample the underrepresented group (in this
case, women).""")
if feature == "capital_gain / capital_loss":
print(
"""Both 'capital_gain' and 'capital_loss' have very low variance, which might
suggest they don't contribute a whole lot of information for predicting income. It
may be okay to omit these features rather than giving the model more noise.""")
if feature == "age":
print(
'''"age" has a lot of variance, so it might benefit from bucketing to learn
fine-grained correlations between income and age, as well as to prevent
overfitting.''')


Both 'capital_gain' and 'capital_loss' have very low variance, which might
suggest they don't contribute a whole lot of information for predicting income. It
may be okay to omit these features rather than giving the model more noise.


## Prediction Using TensorFlow Estimators

Now that we have a better sense of the Adult dataset, we can now begin with creating a neural network to predict income. In this section, we will be using TensorFlow’s Estimator API to access the DNNClassifier class.

We first have to define our input fuction, which will take the Adult dataset that is in a pandas DataFrame and converts it into tensors using the tf.estimator.inputs.pandas_input_fn() function.

def csv_to_pandas_input_fn(data, batch_size=100, num_epochs=1, shuffle=False):
return tf.estimator.inputs.pandas_input_fn(
x=data.drop('income_bracket', axis=1),
y=data['income_bracket'].apply(lambda x: ">50K" in x).astype(int),
batch_size=batch_size,
num_epochs=num_epochs,
shuffle=shuffle,

print 'csv_to_pandas_input_fn() defined.'

csv_to_pandas_input_fn() defined.


TensorFlow requires that data maps to a model. To accomplish this, you have to use tf.feature_columns to ingest and represent features in TensorFlow.

#@title Categorical Feature Columns

# Since we don't know the full range of possible values with occupation and
# native_country, we'll use categorical_column_with_hash_bucket() to help map
# each feature string into an integer ID.
occupation = tf.feature_column.categorical_column_with_hash_bucket(
"occupation", hash_bucket_size=1000)
native_country = tf.feature_column.categorical_column_with_hash_bucket(
"native_country", hash_bucket_size=1000)

# For the remaining categorical features, since we know what the possible values
# are, we can be more explicit and use categorical_column_with_vocabulary_list()
gender = tf.feature_column.categorical_column_with_vocabulary_list(
"gender", ["Female", "Male"])
race = tf.feature_column.categorical_column_with_vocabulary_list(
"race", [
"White", "Asian-Pac-Islander", "Amer-Indian-Eskimo", "Other", "Black"
])
education = tf.feature_column.categorical_column_with_vocabulary_list(
"education", [
"Some-college", "Assoc-acdm", "Assoc-voc", "7th-8th",
"Doctorate", "Prof-school", "5th-6th", "10th", "1st-4th",
"Preschool", "12th"
])
marital_status = tf.feature_column.categorical_column_with_vocabulary_list(
"marital_status", [
"Married-civ-spouse", "Divorced", "Married-spouse-absent",
"Never-married", "Separated", "Married-AF-spouse", "Widowed"
])
relationship = tf.feature_column.categorical_column_with_vocabulary_list(
"relationship", [
"Husband", "Not-in-family", "Wife", "Own-child", "Unmarried",
"Other-relative"
])
workclass = tf.feature_column.categorical_column_with_vocabulary_list(
"workclass", [
"Self-emp-not-inc", "Private", "State-gov", "Federal-gov",
"Local-gov", "?", "Self-emp-inc", "Without-pay", "Never-worked"
])

print 'Categorical feature columns defined.'

Categorical feature columns defined.

#@title Numeric Feature Columns
# For Numeric features, we can just call on feature_column.numeric_column()
# to use its raw value instead of having to create a map between value and ID.
age = tf.feature_column.numeric_column("age")
fnlwgt = tf.feature_column.numeric_column("fnlwgt")
education_num = tf.feature_column.numeric_column("education_num")
capital_gain = tf.feature_column.numeric_column("capital_gain")
capital_loss = tf.feature_column.numeric_column("capital_loss")
hours_per_week = tf.feature_column.numeric_column("hours_per_week")

print 'Numeric feature columns defined.'

Numeric feature columns defined.


If you chose age when completing FairAware Task #3, you noticed that we suggested that age might benefit from bucketing (also known as binning), grouping together similar ages into different groups. This might help the model generalize better across age. As such, we will convert age from a numeric feature (technically, an ordinal feature) to a categorical feature.

age_buckets = tf.feature_column.bucketized_column(
age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])


#### Consider Key Subgroups

When performing feature engineering, it’s important to keep in mind that you may be working with data drawn from individuals belonging to subgroups, for which you’ll want to evaluate model performance separately.

NOTE: In this context, a subgroup is defined as a group of individuals who share a given characteristic—such as race, gender, or sexual orientation—that merits special consideration when evaluating a model with fairness in mind.

When we want our models to mitigate, or leverage, the learned signal of a characteristic pertaining to a subgroup, we will want to use different kinds of tools and techniques—most of which are still open research at this point.

As you work with different variables and define tasks for them, it can be useful to think about what comes next. For example, where are the places where the interaction of the variable and the task could be a concern?

Now we can explicitly define which feature we will include in our model.

We’ll consider gender a subgroup and save it in a separate subgroup_variables list, so we can add special handling for it as needed.

# List of variables, with special handling for gender subgroup.
variables = [native_country, education, occupation, workclass,
relationship, age_buckets]
subgroup_variables = [gender]
feature_columns = variables + subgroup_variables


With the features now ready to go, we can try predicting income using deep learning.

For the sake of simplicity, we are going to keep the neural network architecture light by simply defining a feed-forward neural network with two hidden layers.

But first, we have to convert our high-dimensional categorical features into a low-dimensional and dense real-valued vector, which we call an embedding vector. Luckily, indicator_column (think of it as one-hot encoding) and embedding_column (that converts sparse features into dense features) helps us streamline the process.

The following cell creates the deep columns needed to move forward with defining the model.

deep_columns = [
tf.feature_column.indicator_column(workclass),
tf.feature_column.indicator_column(education),
tf.feature_column.indicator_column(age_buckets),
tf.feature_column.indicator_column(gender),
tf.feature_column.indicator_column(relationship),
tf.feature_column.embedding_column(native_country, dimension=8),
tf.feature_column.embedding_column(occupation, dimension=8),
]

print deep_columns
print 'Deep columns created.'

[IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='workclass', vocabulary_list=('Self-emp-not-inc', 'Private', 'State-gov', 'Federal-gov', 'Local-gov', '?', 'Self-emp-inc', 'Without-pay', 'Never-worked'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='education', vocabulary_list=('Bachelors', 'HS-grad', '11th', 'Masters', '9th', 'Some-college', 'Assoc-acdm', 'Assoc-voc', '7th-8th', 'Doctorate', 'Prof-school', '5th-6th', '10th', '1st-4th', 'Preschool', '12th'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=BucketizedColumn(source_column=NumericColumn(key='age', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), boundaries=(18, 25, 30, 35, 40, 45, 50, 55, 60, 65))), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='gender', vocabulary_list=('Female', 'Male'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), IndicatorColumn(categorical_column=VocabularyListCategoricalColumn(key='relationship', vocabulary_list=('Husband', 'Not-in-family', 'Wife', 'Own-child', 'Unmarried', 'Other-relative'), dtype=tf.string, default_value=-1, num_oov_buckets=0)), EmbeddingColumn(categorical_column=HashedCategoricalColumn(key='native_country', hash_bucket_size=1000, dtype=tf.string), dimension=8, combiner='mean', initializer=<tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7f6effa4cd90>, ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None, trainable=True), EmbeddingColumn(categorical_column=HashedCategoricalColumn(key='occupation', hash_bucket_size=1000, dtype=tf.string), dimension=8, combiner='mean', initializer=<tensorflow.python.ops.init_ops.TruncatedNormal object at 0x7f6effa4cc90>, ckpt_to_load_from=None, tensor_name_in_ckpt=None, max_norm=None, trainable=True)]
Deep columns created.


With all our data preprocessing taken care of, we can now define the deep neural net model. Start by using the parameters defined below. (Later on, after you’ve defined evaluation metrics and evaluated the model, you can come back and tweak these parameters to compare results.)

#@title Define Deep Neural Net Model

HIDDEN_UNITS = [1024, 512] #@param
LEARNING_RATE = 0.1 #@param
L1_REGULARIZATION_STRENGTH = 0.0001 #@param
L2_REGULARIZATION_STRENGTH = 0.0001 #@param

model_dir = tempfile.mkdtemp()
feature_columns=deep_columns,
hidden_units=HIDDEN_UNITS,
learning_rate=LEARNING_RATE,
l1_regularization_strength=L1_REGULARIZATION_STRENGTH,
l2_regularization_strength=L2_REGULARIZATION_STRENGTH),
model_dir=model_dir)

print 'Deep neural net model defined.'

INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_num_ps_replicas': 0, '_keep_checkpoint_max': 5, '_task_type': 'worker', '_global_id_in_cluster': 0, '_is_chief': True, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f6effa4ca50>, '_model_dir': '/tmp/tmpUACkur', '_protocol': None, '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_service': None, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_tf_random_seed': None, '_save_summary_steps': 100, '_device_fn': None, '_experimental_distribute': None, '_num_worker_replicas': 1, '_task_id': 0, '_log_step_count_steps': 100, '_evaluation_master': '', '_eval_distribute': None, '_train_distribute': None, '_master': ''}
Deep neural net model defined.


To keep things simple, we will train for 1000 steps—but feel free to play around with this parameter.

#@title Fit Deep Neural Net Model to the Adult Training Dataset

STEPS = 1000 #@param

input_fn=csv_to_pandas_input_fn(train_df, num_epochs=None, shuffle=True),
steps=STEPS);

print "Deep neural net model is done fitting."

INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/monitored_session.py:809: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.
Instructions for updating:
To construct input pipelines, use the tf.data module.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpUACkur/model.ckpt.
INFO:tensorflow:loss = 69.46589, step = 1
INFO:tensorflow:global_step/sec: 50.047
INFO:tensorflow:loss = 29.053595, step = 101 (2.001 sec)
INFO:tensorflow:global_step/sec: 50.8676
INFO:tensorflow:loss = 31.508945, step = 201 (1.966 sec)
INFO:tensorflow:global_step/sec: 51.0289
INFO:tensorflow:loss = 40.033344, step = 301 (1.959 sec)
INFO:tensorflow:global_step/sec: 50.8928
INFO:tensorflow:loss = 28.343338, step = 401 (1.965 sec)
INFO:tensorflow:global_step/sec: 50.8383
INFO:tensorflow:loss = 36.626408, step = 501 (1.968 sec)
INFO:tensorflow:global_step/sec: 50.0787
INFO:tensorflow:loss = 49.052017, step = 601 (1.996 sec)
INFO:tensorflow:global_step/sec: 49.8781
INFO:tensorflow:loss = 35.097115, step = 701 (2.006 sec)
INFO:tensorflow:global_step/sec: 51.1602
INFO:tensorflow:loss = 31.40864, step = 801 (1.958 sec)
INFO:tensorflow:global_step/sec: 50.9899
INFO:tensorflow:loss = 28.858358, step = 901 (1.956 sec)
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmpUACkur/model.ckpt.
INFO:tensorflow:Loss for final step: 39.20506.
Deep neural net model is done fitting.


We can now evalute the overall model’s performance using the held-out test set.

#@title Evaluate Deep Neural Net Performance

input_fn=csv_to_pandas_input_fn(test_df, num_epochs=1, shuffle=False),
steps=None)
print("model directory = %s" % model_dir)
print("---- Results ----")
for key in sorted(results):
print("%s: %s" % (key, results[key]))

INFO:tensorflow:Calling model_fn.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/metrics_impl.py:2002: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
WARNING:tensorflow:Trapezoidal rule is known to produce incorrect PR-AUCs; please switch to "careful_interpolation" instead.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2019-03-31T13:16:10Z
INFO:tensorflow:Graph was finalized.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
INFO:tensorflow:Restoring parameters from /tmp/tmpUACkur/model.ckpt-1000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Finished evaluation at 2019-03-31-13:16:12
INFO:tensorflow:Saving dict for global step 1000: accuracy = 0.8337317, accuracy_baseline = 0.7543161, auc = 0.88341033, auc_precision_recall = 0.70370615, average_loss = 0.35857627, global_step = 1000, label/mean = 0.24568394, loss = 35.76264, precision = 0.6929032, prediction/mean = 0.25021082, recall = 0.58054054
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1000: /tmp/tmpUACkur/model.ckpt-1000
model directory = /tmp/tmpUACkur
---- Results ----
accuracy: 0.8337317
accuracy_baseline: 0.7543161
auc: 0.88341033
auc_precision_recall: 0.70370615
average_loss: 0.35857627
global_step: 1000
label/mean: 0.24568394
loss: 35.76264
precision: 0.6929032
prediction/mean: 0.25021082
recall: 0.58054054


You can try retraining the model using different parameters. In the end, you will find that a deep neural net does a decent job in predicting income.

But what is missing here is evaluation metrics with respect to subgroups.

## Evaluating for Fairness Using a Confusion Matrix

While evaluating the overall performance of the model gives us some insight into its quality, it doesn’t give us much insight into how well our model performs for different subgroups.

When evaluating a model for fairness, it’s important to determine whether prediction errors are uniform across subgroups or whether certain subgroups are more susceptible to certain prediction errors than others.

A key tool for comparing the prevalence of different types of model errors is a confusion matrix. Recall from the Classification module of Machine Learning Crash Course that a confusion matrix is a grid that plots predictions vs. ground truth for your model, and tabulates statistics summarizing how often your model made the correct prediction and how often it made the wrong prediction.

Let’s start by creating a binary confusion matrix for our income-prediction model—binary because our label (income_bracket) has only two possible values (<50K or >50K). We’ll define an income of >50K as our positive label, and an income of <50k as our negative label.

NOTE: Positive and negative in this context should not be interpreted as value judgments (we are not suggesting that someone who earns more than 50k a year is a better person than someone who earns less than 50k). They are just standard terms used to distinguish between the two possible predictions the model can make.

Cases where the model makes the correct prediction (the prediction matches the ground truth) are classified as true, and cases where the model makes the wrong prediction are classified as false.

Our confusion matrix thus represents four possible states:

• true positive: Model predicts >50K, and that is the ground truth.
• true negative: Model predicts <50K, and that is the ground truth.
• false positive: Model predicts >50K, and that contradicts reality.
• false negative: Model predicts <50K, and that contradicts reality.

NOTE: If desired, we can use the number of outcomes in each of these states to calculate secondary evaluation metrics, such as precision and recall.

### Plot the Confusion Matrix

The following cell define a function that uses the sklearn.metrics.confusion_matrix module to calculate all the instances (true positive, true negative, false positive, and false negative) needed to compute our binary confusion matrix and evaluation metrics.

#@test {"output": "ignore"}
#@title Define Function to Compute Binary Confusion Matrix Evaluation Metrics
def compute_eval_metrics(references, predictions):
tn, fp, fn, tp = confusion_matrix(references, predictions).ravel()
precision = tp / float(tp + fp)
recall = tp / float(tp + fn)
false_positive_rate = fp / float(fp + tn)
false_omission_rate = fn / float(tn + fn)
return precision, recall, false_positive_rate, false_omission_rate

print 'Binary confusion matrix and evaluation metrics defined.'

Binary confusion matrix and evaluation metrics defined.


We will also need help plotting the binary confusion matrix. The function below combines various third-party modules (pandas DataFrame, Matplotlib, Seaborn) to draw the confusion matrix.

#@title Define Function to Visualize Binary Confusion Matrix
def plot_confusion_matrix(confusion_matrix, class_names, figsize = (8,6)):
# We're taking our calculated binary confusion matrix that's already in form
# of an array and turning it into a Pandas DataFrame because it's a lot
# easier to work with when visualizing a heat map in Seaborn.
df_cm = pd.DataFrame(
confusion_matrix, index=class_names, columns=class_names,
)
fig = plt.figure(figsize=figsize)

# Combine the instance (numercial value) with its description
strings = np.asarray([['True Positives', 'False Negatives'],
['False Positives', 'True Negatives']])
labels = (np.asarray(
["{0:d}\n{1}".format(value, string) for string, value in zip(
strings.flatten(), confusion_matrix.flatten())])).reshape(2, 2)

heatmap = sns.heatmap(df_cm, annot=labels, fmt="");
heatmap.yaxis.set_ticklabels(
heatmap.yaxis.get_ticklabels(), rotation=0, ha='right')
heatmap.xaxis.set_ticklabels(
heatmap.xaxis.get_ticklabels(), rotation=45, ha='right')
plt.ylabel('References')
plt.xlabel('Predictions')
return fig

print "Binary confusion matrix visualization defined."

Binary confusion matrix visualization defined.


Now that we have all the necessary functions defined, we can now compute the binary confusion matrix and evaluation metrics using the outcomes from our deep neural net model. The output of this cell is a tabbed view, which allows us to toggle between the confusion matrix and evaluation metrics table.

Are there any significant disparities in error rates that suggest the model performs better for one subgroup than another?

#@title Visualize Binary Confusion Matrix and Compute Evaluation Metrics Per Subgroup
CATEGORY  =  "gender" #@param {type:"string"}
SUBGROUP =  "Male" #@param {type:"string"}

# Given define subgroup, generate predictions and obtain its corresponding
# ground truth.
test_df.loc[test_df[CATEGORY] == SUBGROUP], num_epochs=1, shuffle=False))
predictions = []
for prediction_item, in zip(predictions_dict):
predictions.append(prediction_item['class_ids'][0])
actuals = list(
test_df.loc[test_df[CATEGORY] == SUBGROUP]['income_bracket'].apply(
lambda x: '>50K' in x).astype(int))
classes = ['Over $50K', 'Less than$50K']

# To stay consistent, we have to flip the confusion
# matrix around on both axes because sklearn's confusion matrix module by
# default is rotated.
rotated_confusion_matrix = np.fliplr(confusion_matrix(actuals, predictions))
rotated_confusion_matrix = np.flipud(rotated_confusion_matrix)

tb = widgets.TabBar(['Confusion Matrix', 'Evaluation Metrics'], location='top')

with tb.output_to('Confusion Matrix'):
plot_confusion_matrix(rotated_confusion_matrix, classes);

with tb.output_to('Evaluation Metrics'):
grid = widgets.Grid(2,4)

p, r, fpr, fomr = compute_eval_metrics(actuals, predictions)

with grid.output_to(0, 0):
print " Precision "
with grid.output_to(1, 0):
print " %.4f " % p

with grid.output_to(0, 1):
print " Recall "
with grid.output_to(1, 1):
print " %.4f " % r

with grid.output_to(0, 2):
print " False Positive Rate "
with grid.output_to(1, 2):
print " %.4f " % fpr

with grid.output_to(0, 3):
print " False Omission Rate "
with grid.output_to(1, 3):
print " %.4f " % fomr

INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpUACkur/model.ckpt-1000
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.


Precision: 0.6904
Recall: 0.6131
False Positive Rate: 0.1234
False Omission Rate: 0.1653

In your work, make sure that you make a good decision about the tradeoffs between false positives, false negatives, true positives, and true negatives. For example, you may want a very low false positive rate, but a high true positive rate. Or you may want a high precision, but a low recall is okay.