Need help with evaluating model!

Hi I am new to this, I am successfully able to train and evaluate my model, however now I am wondering how do I recompute the same metrics and performance gap using fairness indicators.

My model is something like this:

def model_func():   
    model = tf.keras.models.Sequential([
        keras.layers.Dense(units = 14, input_dim=14, activation='relu'),
        keras.layers.Dense(units = 28, activation='relu'),
        keras.layers.Dense(units = 1,  activation='sigmoid')
        ])

    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

    return model

Then I train model and test it on test data-set.

# Geting my trained model
model = model_func()

# Training my model
train = model.fit(X_train, y_train, epochs=50, batch_size=10, verbose = 1)

Now how do I recompute the same metrics and performance gap using fairness indicators?

Start with the model_func definition and the model.fit call shown in the issue. Determine which trained-model outputs, labels, and fairness indicators are required to reproduce the reported metrics and performance gap. Done means the evaluation inputs and expected fairness results are clearly documented.