aiverify-veritastool 2.2.1

Veritas Diagnosis tool for fairness & transparency assessment.

Veritas Toolkit

The purpose of this toolkit is to facilitate the adoption of Veritas Methodology on Fairness & Transparency Assessment and spur industry development. It will also benefit customers by improving the fairness and transparency of financial services delivered by AIDA systems.

As a AI Verify stock-plugin, this updates the existing Veritas Toolkit to be compatible with the AI Verify schema and plugin structure.

License

• Licensed under Apache Software License 2.0

Developers:

• AI Verify
• MAS Veritas
• Resaro

Plugin Content

• Algorithms

Name	Description
Veritastool	Veritas Diagnosis tool for fairness & transparency assessment

Installation

The easiest way to install veritastool is to download it from PyPI. It's going to install the library itself and its prerequisites as well. It is suggested to create virtual environment with requirements.txt file first.

pip install aiverify-veritastool

Migrating from the existing Veritas Toolkit

The existing Veritas Toolkit has been updated to be compatible with the AI Verify schema and plugin structure. Users with model artifact json files from the existing Veritas Toolkit can convert them to the new format using the convert_veritas_artifact_to_aiverify function.

from aiverify_veritastool.util.aiverify import convert_veritas_artifact_to_aiverify

convert_veritas_artifact_to_aiverify(model_artifact_path="old_model_artifact_path.json", output_dir="output")

The test results will be saved in the output directory and can subsequently be updated into the AI Verify API gateway and platform.

Using the Plugin in Jupyter Notebook

Similar to the existing veritastool library, the updated plugin can be used as part of a Jupyter Notebook development workflow.

You can now import the custom library that you would to use for diagnosis. In this example we will use the Credit Scoring custom library.

from veritastool.model.modelwrapper import ModelWrapper
from veritastool.model.model_container import ModelContainer
from veritastool.usecases.credit_scoring import CreditScoring

Once the relevant use case object (CreditScoring) and model container (ModelContainer) has been imported, you can upload your contents into the container and initialize the object for diagnosis.

import pickle
import numpy as np

#Load Credit Scoring Test Data
# NOTE: Assume that the stock_plugin/user_defined_files/veritas_data folder is copied to the current working directory
file = "./veritas_data/credit_score_dict.pickle"
input_file = open(file, "rb")
cs = pickle.load(input_file)

#Model Contariner Parameters
y_true = np.array(cs["y_test"])
y_pred = np.array(cs["y_pred"])
y_train = np.array(cs["y_train"])
p_grp = {'SEX': [1], 'MARRIAGE':[1]}
up_grp = {'SEX': [2], 'MARRIAGE':[2]}
x_train = cs["X_train"]
x_test = cs["X_test"]
model_name = "credit_scoring"
model_type = "classification"
y_prob = cs["y_prob"]
model_obj = LogisticRegression(C=0.1)
model_obj.fit(x_train, y_train) #fit the model as required for transparency analysis

#Create Model Container
container = ModelContainer(y_true, p_grp, model_type, model_name, y_pred, y_prob, y_train, x_train=x_train, \
                           x_test=x_test, model_object=model_obj, up_grp=up_grp)

#Create Use Case Object
cre_sco_obj= CreditScoring(model_params = [container], fair_threshold = 80, fair_concern = "eligible", \
                           fair_priority = "benefit", fair_impact = "normal", perf_metric_name="accuracy", \
                           tran_row_num = [20,40], tran_max_sample = 1000, tran_pdp_feature = ['LIMIT_BAL'], tran_max_display = 10)

ModelContainer

The ModelContainer class is a helper class that holds all the model parameters required for computations in all use cases. It acts as a data holder for model details, input data, and metadata.

ModelContainer Parameters

Here are the main parameters of the ModelContainer class:

Parameter	Type	Required	Default	Description
y_true	list, np.ndarray, pd.Series	Yes	None	Ground truth target values
p_grp	dict of lists	Yes	None	Dictionary of privileged groups for protected variables
model_type	str	Yes	"classification"	Type of model: "classification", "regression", or "uplift"
model_name	str	No	"auto"	Name for the model artifact json file
y_pred	list, np.ndarray, pd.Series	No	None	Predicted targets as returned by classifier
y_prob	list, np.ndarray, pd.Series, pd.DataFrame	No	None	Predicted probabilities as returned by classifier
y_train	list, np.ndarray, pd.Series	No	None	Ground truth for training data
x_train	pd.DataFrame, str	No	None	Training dataset
x_test	pd.DataFrame, str	No	None	Testing dataset
model_object	object	No	None	Trained model object for feature importance
up_grp	dict	No	None	Dictionary of unprivileged groups for protected variables
sample_weight	list, np.ndarray	No	None	Weights for normalizing y_true & y_pred
pos_label	list	No	[1]	Label values considered favorable
neg_label	list	No	None	Label values considered unfavorable (required for uplift models)

Use Cases

The Veritas Toolkit provides various use cases for different applications:

1. Base Classification
2. Base Regression
3. Customer Marketing
4. Credit Scoring
5. Predictive Underwriting

Each use case is a wrapper around the Fairness and Transparency classes, which are responsible for computing the respective metrics and analyses. The use cases take in the model container as input using the model_params parameter. Here's an example of the parameters of a use case:

Fairness Parameters

Parameter	Type	Required	Default	Description
fair_threshold	int, float	No	80	Fairness threshold value (0-100)
perf_metric_name	str	No	"balanced_acc"	Primary performance metric used in evaluate() and/or compile() functions
fair_metric_name	str	No	"auto"	Primary performance metric used in evaluate() and/or compile() functions
fair_concern	str	No	"eligible"	Fairness concern: "eligible", "inclusive", or "both"
fair_priority	str	No	"benefit"	Fairness priority: "benefit" or "harm"
fair_impact	str	No	"normal"	Fairness impact: "normal", "significant", or "selective"
fair_metric_type	str	No	"difference"	Fairness metric type: "difference" or "ratio"

Performance Metrics (perf_metric_name)

The performance metrics available depend on the model type. Here are the supported metrics:

Model Type	Available Metrics
Classification	"accuracy", "balanced_acc", "f1_score", "precision", "recall", "tnr", "fnr", "npv", "roc_auc", "log_loss", "selection_rate"
Regression	"rmse", "mape", "wape"
Uplift	"emp_lift", "expected_profit", "expected_selection_rate"

Fairness Metrics (fair_metric_name)

When fair_metric_name is set to "auto", the toolkit uses the Fairness Tree methodology to determine the appropriate metric based on fair_concern, fair_priority, and fair_impact. You can also directly specify a fairness metric:

Difference-based Metrics

These metrics measure the arithmetic difference between privileged and unprivileged groups:

Metric Name	Description
demographic_parity	Difference in approval rates
equal_opportunity	Difference in true positive rates
fpr_parity	Difference in false positive rates
tnr_parity	Difference in true negative rates
fnr_parity	Difference in false negative rates
ppv_parity	Difference in positive predictive values
npv_parity	Difference in negative predictive values
fdr_parity	Difference in false discovery rates
for_parity	Difference in false omission rates
equal_odds	Difference in equalized odds
neg_equal_odds	Difference in negative equalized odds
calibration_by_group	Difference in calibration by group
auc_parity	Difference in AUC
log_loss_parity	Difference in log loss
rmse_parity	Difference in root mean squared error (regression)
mape_parity	Difference in mean absolute percentage error (regression)
wape_parity	Difference in weighted absolute percentage error (regression)
rejected_harm	Difference in harm from rejection (uplift)
acquire_benefit	Difference in benefit from acquiring (uplift)

Ratio-based Metrics

These metrics measure the ratio between unprivileged and privileged groups:

Metric Name	Description
disparate_impact	Ratio of approval rates
equal_opportunity_ratio	Ratio of true positive rates
fpr_ratio	Ratio of false positive rates
tnr_ratio	Ratio of true negative rates
fnr_ratio	Ratio of false negative rates
ppv_ratio	Ratio of positive predictive values
npv_ratio	Ratio of negative predictive values
fdr_ratio	Ratio of false discovery rates
for_ratio	Ratio of false omission rates
equal_odds_ratio	Ratio of equalized odds
neg_equal_odds_ratio	Ratio of negative equalized odds
calibration_by_group_ratio	Ratio of calibration by group
auc_ratio	Ratio of AUC
log_loss_ratio	Ratio of log loss
rmse_ratio	Ratio of root mean squared error (regression)
mape_ratio	Ratio of mean absolute percentage error (regression)
wape_ratio	Ratio of weighted absolute percentage error (regression)

Fairness Concern (fair_concern)

This parameter helps determine what type of fairness concern is being addressed:

Value	Description
"eligible"	Focuses on whether individuals who are eligible for a given outcome are treated fairly
"inclusive"	Focuses on whether individuals who are not eligible for a given outcome (e.g. rejected applications) are treated fairly
"both"	Considers fairness for both accepted and rejected individuals

Fairness Priority (fair_priority)

This parameter helps determine what should be prioritized in the fairness assessment:

Value	Description
"benefit"	Prioritizes ensuring that benefits are distributed fairly
"harm"	Prioritizes ensuring that harms are distributed fairly

Fairness Impact (fair_impact)

This parameter helps determine the context and severity of the fairness impact:

Value	Description
"normal"	Standard impact context with no special considerations; selects for basic rate parity metrics
"significant"	Contexts where decisions have major consequences; selects metrics that focus on predictive value parity
"selective"	Identical as selecting significant

Transparency Parameters

Parameter	Type	Required	Default	Description
tran_row_num	list	No	[1]	Row indices for local interpretability
tran_max_sample	int, float	No	1	Number/percent of records to sample
tran_pdp_feature	list	No	[]	Features for partial dependence plots
tran_max_display	int	No	10	Number of features to display in output

Special Parameters

Each use case has its own set of special parameters that are specific to the use case. For example, the CreditScoring use case takes in additional parameters for num_applicants and base_default_rate:

Parameter	Type	Description
num_applicants	dict	Counts of rejected applicants for privileged/unprivileged groups
base_default_rate	dict	Base default rates for privileged/unprivileged groups

while CustomerMarketing accepts the following parameters:

Parameter	Type	Description
treatment_cost	int, float	Cost of the marketing treatment per customer
revenue	int, float	Revenue gained per customer

For more information, refer to the docstrings of the respective classes and use cases.

API functions

Below are the API functions that the user can execute to obtain the fairness and transparency diagnosis of their use cases.

Evaluate

The evaluate API function computes all performance and fairness metrics and renders it in a table format (default). It also highlights the primary performance and fairness metrics (automatic if not specified by user).

cre_sco_obj.evaluate()

Output:

You can also toggle the widget to view your results in a interactive visualization format.

cre_sco_obj.evaluate(visualize = True)

Output:

Tradeoff

Computes trade-off between performance and fairness.

cre_sco_obj.tradeoff()

Output:

** Note: Replace {Balanced Accuracy} with the respective given metrics.

Feature Importance

Computes feature importance of protected features using leave one out analysis.

cre_sco_obj.feature_importance()

Output:

Note

The fairness conclusion shows how removing a protected variable affects the fairness of the model:

The first part (e.g., "fair to fair") indicates the transition from:

The baseline fairness conclusion (with all variables)
To the new fairness conclusion after removing a specific protected variable
The symbols "(+)" or "(-)" provide additional information:

(+) means the model is now fairer after removing the variable
(-) means the model is now less fair after removing the variable

The "suggestion" field provides recommendations on whether to include or exclude a protected variable from the model, based on the tradeoff between fairness and performance:

- "include": The variable should be kept in the model. This is recommended when:
Removing the variable makes the model less fair and;
Removing hurts performance

• "exclude": The variable should be removed from the model. This is recommended when: Removing the variable makes the model fairer and; Removing improves performance

• "examine further": More analysis is needed. This happens when there's a tradeoff between fairness and performance i.e. removing a variable improves fairness but hurts performance, or vice versa.

Root Cause

Computes the importance of variables contributing to the bias.

cre_sco_obj.root_cause()

Output:

Mitigate

User can choose methods to mitigate the bias.

mitigated = cre_sco_obj.mitigate(p_var=[], method=['reweigh', 'correlation', 'threshold'])

Output:

Explain

Runs the transparency analysis - global & local interpretability, partial dependence analysis and permutation importance

#run the entire transparency analysis
cre_sco_obj.explain()

Output:

#get the local interpretability plot for specific row index and model
cre_sco_obj.explain(local_row_num = 20)

Output:

Compile

Generates model artifact file output. This function also runs all the API functions if it hasn't already been run.

output = cre_sco_obj.compile(save_artifact=False)

Output:

Model Artifact

In place of the previous model artifact file, users can now generate a test output that is compatible with the AI Verify schema.

from aiverify_veritastool.util.aiverify import convert_veritas_artifact_to_aiverify

output = cre_sco_obj.compile(save_artifact=False)
convert_veritas_artifact_to_aiverify(model_artifact_path=output, output_dir="output")

The test results will be saved in the output directory and can subsequently be updated into the AI Verify API gateway and platform.

Examples

You may refer to our example notebooks below to see how the toolkit can be applied:

Filename	Description
CS_Demo.ipynb	Tutorial notebook to diagnose a credit scoring model for predicting customers' loan repayment.
CM_Demo.ipynb	Tutorial notebook to diagnose a customer marketing uplift model for selecting existing customers for a marketing call to increase the sales of loan product.
BaseClassification_demo.ipynb	Tutorial notebook for a multi-class propensity model
BaseRegression_demo.ipynb	Tutorial notebook for a prediciton of a continuous target variable
PUW_demo.ipynb	Tutorial notebook for a binary classification model to predict whether to award insurance policy by assessing risk

Running the plugin via CLI

Once the finalised parameters are decided, one can validate the results and generate an output that is compatible with the AI Verify platform by running the plugin via CLI. Here's an example bash script to execute the plugin via CLI:

#!/bin/bash

root_path="<PATH_TO_FOLDER>/aiverify/stock-plugins/user_defined_files/veritas_data"

aiverify_veritastool \
  --data_path $root_path/cs_X_test.pkl \
  --model_path $root_path/cs_model.pkl \
  --ground_truth_path $root_path/cs_y_test.pkl \
  --ground_truth y_test \
  --training_data_path $root_path/cs_X_train.pkl \
  --training_ground_truth_path $root_path/cs_y_train.pkl \
  --training_ground_truth y_train \
  --use_case "base_regression" \
  --privileged_groups '{"SEX": [1], "MARRIAGE": [1]}' \
  --model_type CLASSIFICATION \
  --fair_threshold 80 \
  --fair_metric "auto" \
  --fair_concern "eligible" \
  --performance_metric "accuracy" \
  --transparency_rows 20 40 \
  --transparency_max_samples 1000 \
  --transparency_features LIMIT_BAL \
  --run_pipeline

If the algorithm runs successfully, the results of the test will be saved in an output folder.

Build Plugin

cd aiverify/stock-plugins/aiverify.stock.veritas/algorithms/veritastool
hatch build

Tests

Pytest is used as the testing framework.

Run the following steps to execute the unit and integration tests inside the tests/ folder

cd aiverify/stock-plugins/aiverify.stock.veritas/algorithms/veritastool
pytest tests/util tests/metrics tests/models tests/principles tests/usecases -p no:warnings

Run using Docker

In the aiverify root directory, run the following command to build the docker image

docker build -t aiverify-veritastool -f ./stock-plugins/aiverify.stock.veritas/algorithms/veritastool/Dockerfile .

Modify the parameters accordingly and run the example bash script:

#!/bin/bash
docker run \
  -v $(pwd)/stock-plugins/user_defined_files:/input \
  -v $(pwd)/stock-plugins/aiverify.stock.veritas/algorithms/veritastool/output:/app/aiverify/output \
  aiverify-veritastool \
  --data_path /input/veritas_data/cs_X_test.pkl \
  --model_path /input/veritas_data/cs_model.pkl \
  --ground_truth_path /input/veritas_data/cs_y_test.pkl \
  --ground_truth y_test \
  --training_data_path /input/veritas_data/cs_X_train.pkl \
  --training_ground_truth_path /input/veritas_data/cs_y_train.pkl \
  --training_ground_truth y_train \
  --use_case "base_regression" \
  --privileged_groups '{"SEX": [1], "MARRIAGE": [1]}' \
  --model_type CLASSIFICATION \
  --fair_threshold 80 \
  --fair_metric "auto" \
  --fair_concern "eligible" \
  --performance_metric "accuracy" \
  --transparency_rows 20 40 \
  --transparency_max_samples 1000 \
  --transparency_features LIMIT_BAL \
  --run_pipeline

If the algorithm runs successfully, the results of the test will be saved in an output folder in the algorithm directory.