eticas-audit 0.1.7

An open-source Python library designed for developers to calculate fairness metrics and assess bias in machine learning models.

Eticas: Bias & Audit Framework

An open-source Python library designed for developers to calculate fairness metrics and assess bias in machine learning models. This library provides a comprehensive set of tools to ensure transparency, accountability, and ethical AI development.

Website • Key Features • Installation • Metrics • Example Notebooks • QuickStart Bias Auditing • Explore Results •

Why Use This Library?

AI System can inherit biases from data or amplify them during decision-making processes. This library helps ensure transparency and accountability by providing actionable insights to improve fairness in AI systems.

🚀 Key Features

This framework is designed to audit AI systems comprehensively across all stages of their lifecycle. At its core, it focuses on comparing privileged and underprivileged groups, ensuring a fair evaluation of model behavior.

With a wide range of metrics, this framework is a game-changer in bias monitoring. It offers a deep perspective on fairness, allowing for comprehensive reporting even without relying on true labels. The only restriction for measuring bias in production lies in performance metrics, as they are directly tied to true labels.

The stages considered are the following:

1. The dataset used to train the model.
2. The dataset used in production.
3. A dataset containing the system’s final decisions, which may include human intervention or another model.

• Demographic Benchmarking Monitoring: Perform in-depth analysis of population distribution.
• Model Fairness Monitoring: Ensure equality and detect equity issues in decision-making.
• Features Distribution Evaluation: Analyze correlations, causality, and variable importance.
• Performance Analysis: Metrics to assess model performance, accuracy, and recall.
• Model Drift Monitoring: Detect and measure changes in data distributions and model behavior over time.

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⚖️ Metrics

Group	Metric	Label needed?	Description
fairness	d_equality	no	Analyze whether the system’s disparities occur because the model does not treat all groups equally.
fairness	d_equity	no	Analyze whether the system’s disparities arise because some groups have unique characteristics and may need a boost.
fairness	d_parity	no	Calculate the ratio of Selection Rates (Disparate Impact, DI). It represents the chance of success.
fairness	d_statisticalparity	no	Calculate the difference in Selection Rates (Statistical Parity Difference, SPD). It measures the gap in success rates.
fairness	d_calibrated_false	yes	Evaluate the calibration for negative outcomes across groups.
fairness	d_calibrated_true	yes	Evaluate the calibration for positive outcomes across groups.
fairness	d_equalodds_false	yes	Check whether false outcomes are distributed equally among groups.
fairness	d_equalodds_true	yes	Check whether true outcomes are distributed equally among groups.
Demographic Benchmarking	da_inconsistency	no	Calculate the percentage of samples that belong to an underprivileged group.
Demographic Benchmarking	da_positive	no	Calculate the percentage of samples that receive a positive outcome and belong to an underprivileged group.
Features Distribution	da_informative	no	Determine if there is a proxy feature in the dataset, meaning some features act as a protected attribute.
Features Distribution	dxa_inconsistency	no	Check if the protected attributes are highly related to the output.
Performance	accuracy	yes	Calculate the proportion of correct predictions among all predictions.
Performance	F1	yes	Compute the harmonic mean of precision and recall.
Performance	precision	yes	Compute the ratio of true positives to all predicted positives.
Performance	recall	yes	Compute the ratio of true positives to all actual positives.
Performance	poor_performance	yes	Calculate the accuracy against the representation of the largest class.
Drift	Drift Train-Operational	no	Evaluate changes in data or model performance between training and operational phases.

📥 Installation

pip install eticas-audit

Example Notebooks

Notebook	Description
Audit AI System	Check how use this library to audit AI System.

QuickStart Bias Auditing

Execute Audit

Define Senstive Attributes

Use a JSON object to define the sensitive attributes. You need to specify the columns where the attribute names and the underprivileged or privileged groups are defined. This definition can include a list to accommodate more than one group.

Sensitive attributes can be simple, for example sex or race. They can also be complex—for instance, the intersection of sex and race.

{
    "sensitive_attributes": {
        "sex": {
            "columns": [
                {
                    "name": "sex",
                    "underprivileged": [2]
                }
            ],
            "type": "simple"
        },
        "ethnicity": {
            "columns": [
                {
                    "name": "ethnicity",
                    "privileged": [1]
                }
            ],
            "type": "simple"
        },
        "age": {
            "columns": [
                {
                    "name": "age",
                    "privileged": [3, 4]
                }
            ],
            "type": "simple"
        },
        "sex_ethnicity": {
            "groups": ["sex", "ethnicity"],
            "type": "complex"
        }
    }
}

Create Model

Initialize the model object that will be the focus of the audit. As important inputs, you need to define the sensitive attributes and specify which input features you want to analyze. It's not necessary to include all features—only the most important or relevant ones.

import logging
logging.basicConfig(
    level=logging.INFO,
    format='[%(levelname)s] %(name)s - %(message)s'
)

from eticas.model.ml_model import MLModel
model = MLModel(
    model_name="ML Testing Regression",
    description="A logistic regression model to illustrate audits",
    country="USA",
    state="CA",
    sensitive_attributes=sensitive_attributes,
    features=["feature_0", "feature_1", "feature_2"]
)

Audit Labeled

This is how to define the audit for a labeled dataset. In general the dataset used to train the dataset.The required inputs are:

1. dataset_path – path to the data,
2. label_column – represents the true label,
3. output_column – contains the model’s output,
4. positive_output – A list of outputs considered positive.

You can also upload a label or output column with scoring, ranking, or recommendation values (continuous values). If the regression ordering is ascending, the positive output is interpreted as 1; if it is descending, it is interpreted as 0.

model.run_labeled_audit(dataset_path ='files/example_training_binary_2.csv', 
                        label_column = 'outcome', 
                        output_column = 'predicted_outcome',
                        positive_output = [1])

#json labeled results
model.labeled_results

Audit Production

This is how to define the audit for a production dataset. The required inputs are:

1. dataset_path – path to the data,
2. output_column – contains the model’s output,
3. positive_output – A list of outputs considered positive.

You can also upload a label or output column with scoring, ranking, or recommendation values (continuous values). If the regression ordering is ascending, the positive output is interpreted as 1; if it is descending, it is interpreted as 0.

model.run_production_audit(dataset_path ='files/example_operational_binary_2.csv',
                           output_column = 'predicted_outcome',
                           positive_output = [1])

#json production results
model.production_results

Audit Impacted

This is how to define the audit for a production dataset. The required inputs are:

1. dataset_path – path to the data,
2. output_column – contains the model’s output,
3. positive_output – A list of outputs considered positive.

You can also upload a label or output column with scoring, ranking, or recommendation values (continuous values). If the regression ordering is ascending, the positive output is interpreted as 1; if it is descending, it is interpreted as 0.

model.run_impacted_audit(dataset_path ='files/example_impact_binary_2.csv', 
                         output_column = 'recorded_outcome',
                         positive_output = [1])

#json impacted results
model.impacted_results

Audit Drift

model.run_drift_audit(dataset_path_dev = 'files/example_training_binary_2.csv', 
                      output_column_dev = 'outcome',
                      positive_output_dev = [1],
                      dataset_path_prod = 'files/example_operational_binary_2.csv', 
                      output_column_prod = 'predicted_outcome',
                      positive_output_prod = [1])

#json drift results
model.drift_results

Explore Results

The results can be exported in JSON or DataFrame format. Both options allow you to extract the information with or without normalization. Normalized values range from 0 to 100, where 0 represents a poor result and 100 represents a perfect value.

audit_result = model.df_results(norm_values=True)

audit_result = model.json_results(norm_values=True)

Metrics WITHOUT TRUE LABEL

Fairness

result = audit_result.xs(('fairness',), level=(0,))
result = result.reset_index()
result = result.pivot(
    index=['metric', 'attribute'],  
    columns='stage',             
    values='value'   
)

result

Metric	Attribute	01-labeled	02-production	03-impact
d_equality	age	99.0 ▓▓▓▓▓▓	99.0 ▓▓▓▓▓▓	100.0 ▓▓▓▓▓▓
	ethnicity	100.0 ▓▓▓▓▓▓	99.0 ▓▓▓▓▓▓	44.0 ▓▓▓
	sex	100.0 ▓▓▓▓▓▓	71.0 ▓▓▓▓	47.0 ▓▓▓
	sex_ethnicity	99.0 ▓▓▓▓▓▓	62.0 ▓▓▓▓	44.0 ▓▓▓
d_equity	age	100.0 ▓▓▓▓▓▓	99.0 ▓▓▓▓▓▓	100.0 ▓▓▓▓▓▓
	ethnicity	100.0 ▓▓▓▓▓▓	70.0 ▓▓▓▓	44.0 ▓▓▓
	sex	99.0 ▓▓▓▓▓▓	48.0 ▓▓▓	41.0 ▓▓▓
	sex_ethnicity	100.0 ▓▓▓▓▓▓	57.0 ▓▓▓	44.0 ▓▓▓
d_parity	age	98.0 ▓▓▓▓▓▓	98.0 ▓▓▓▓▓▓	99.0 ▓▓▓▓▓▓
	ethnicity	99.0 ▓▓▓▓▓▓	76.0 ▓▓▓▓	42.0 ▓▓▓
	sex	99.0 ▓▓▓▓▓▓	64.0 ▓▓▓▓	40.0 ▓▓▓
	sex_ethnicity	99.0 ▓▓▓▓▓▓	65.0 ▓▓▓▓	40.0 ▓▓▓
d_statisticalparity	age	98.0 ▓▓▓▓▓▓	98.0 ▓▓▓▓▓▓	98.0 ▓▓▓▓▓▓
	ethnicity	100.0 ▓▓▓▓▓▓	74.0 ▓▓▓▓	43.0 ▓▓▓
	sex	100.0 ▓▓▓▓▓▓	57.0 ▓▓▓	42.0 ▓▓▓
	sex_ethnicity	98.0 ▓▓▓▓▓▓	57.0 ▓▓▓	42.0 ▓▓▓

Benchmarking

result = audit_result.xs(('benchmarking',), level=(0,))
result = result.reset_index()
result = result.pivot(
    index=['metric', 'attribute'],  
    columns='stage',             
    values='value'   
)

result

Metric	Attribute	01-labeled	02-production	03-impact
da_inconsistency	age	44.8 ▓▓▓	44.5 ▓▓▓	45.0 ▓▓▓
	ethnicity	40.0 ▓▓▓	20.0 ▓▓	10.0 ▓
	sex	60.0 ▓▓▓▓	30.0 ▓▓	15.0 ▓
	sex_ethnicity	25.0 ▓▓	15.0 ▓	10.0 ▓
da_positive	age	45.2 ▓▓▓	44.9 ▓▓▓	45.3 ▓▓▓
	ethnicity	39.8 ▓▓▓	15.8 ▓	3.7 ▓
	sex	59.8 ▓▓▓▓	19.7 ▓▓	5.7 ▓
	sex_ethnicity	24.7 ▓▓	10.0 ▓	3.7 ▓

Distribution

result = audit_result.xs(('distribution',), level=(0,))
result = result.reset_index()
result = result.pivot(
    index=['metric', 'attribute'],  
    columns='stage',             
    values='value'   
)

result

Metric	Attribute	01-labeled	02-production	03-impact
d_equality	age	100.0 ▓▓▓▓▓▓	100.0 ▓▓▓▓▓▓	100.0 ▓▓▓▓▓▓
	ethnicity	100.0 ▓▓▓▓▓▓	100.0 ▓▓▓▓▓▓	72.0 ▓▓▓▓
	sex	99.0 ▓▓▓▓▓▓	90.0 ▓▓▓▓▓▓	60.0 ▓▓▓▓
	sex_ethnicity	100.0 ▓▓▓▓▓▓	90.0 ▓▓▓▓▓▓	60.0 ▓▓▓▓
d_equity	age	99.0 ▓▓▓▓▓▓	98.0 ▓▓▓▓▓▓	98.0 ▓▓▓▓▓▓
	ethnicity	99.0 ▓▓▓▓▓▓	89.0 ▓▓▓▓▓▓	77.0 ▓▓▓▓
	sex	99.0 ▓▓▓▓▓▓	77.0 ▓▓▓▓▓▓	70.0 ▓▓▓▓
	sex_ethnicity	98.0 ▓▓▓▓▓▓	86.0 ▓▓▓▓▓▓	77.0 ▓▓▓▓

Drift

result = audit_result.xs(('drift',), level=(0,))
result = result.reset_index()
result = result.pivot(
    index=['metric', 'attribute'],  
    columns='stage',             
    values='value'   
)

result

Metric	Attribute	02-production
drift	age	1.17 ▓
	ethnicity	0.0 ▓
	overall	0.87 ▓
	sex	0.0 ▓
	sex_ethnicity	1.62 ▓

Metrics WITH TRUE LABEL

Fairness

result = audit_result.xs(('fairness_label',), level=(0,))
result = result.reset_index()
result = result.pivot(
    index=['metric', 'attribute'],  
    columns='stage',             
    values='value'   
)

result

Metric	Attribute	01-labeled
d_calibrated_false	age	99.0 ▓▓▓▓▓▓
	ethnicity	97.0 ▓▓▓▓▓▓
	sex	98.0 ▓▓▓▓▓▓
	sex_ethnicity	99.0 ▓▓▓▓▓▓
d_calibrated_true	age	96.0 ▓▓▓▓▓▓
	ethnicity	98.0 ▓▓▓▓▓▓
	sex	96.0 ▓▓▓▓▓▓
	sex_ethnicity	95.0 ▓▓▓▓▓▓
d_equalodds_false	age	99.0 ▓▓▓▓▓▓
	ethnicity	97.0 ▓▓▓▓▓▓
	sex	98.0 ▓▓▓▓▓▓
	sex_ethnicity	99.0 ▓▓▓▓▓▓
d_equalodds_true	age	96.0 ▓▓▓▓▓▓
	ethnicity	98.0 ▓▓▓▓▓▓
	sex	96.0 ▓▓▓▓▓▓
	sex_ethnicity	95.0 ▓▓▓▓▓▓

Performance

result = audit_result.xs(('performance',), level=(0,))
result = result.reset_index()
result = result.pivot(
    index=['metric', 'attribute'],  
    columns='stage',             
    values='value'   
)

result

Metric	Attribute	01-labeled
FN	age	1151
	ethnicity	987
	sex	1475
	sex_ethnicity	619
FP	age	1146
	ethnicity	1027
	sex	1565
	sex_ethnicity	653
TN	age	1060
	ethnicity	973
	sex	1428
	sex_ethnicity	605
TP	age	1121
	ethnicity	1013
	sex	1532
	sex_ethnicity	623
accuracy	age	48.7 ▓▓
	ethnicity	49.65 ▓▓
	sex	49.33 ▓▓
	sex_ethnicity	49.12 ▓▓
f1	age	49.39 ▓▓
	ethnicity	50.15 ▓▓
	sex	50.2 ▓▓
	sex_ethnicity	49.48 ▓▓
poor_performance	age	57.58 ▓▓
	ethnicity	59.58 ▓▓
	sex	59.05 ▓▓
	sex_ethnicity	58.57 ▓▓
precision	age	49.45 ▓▓
	ethnicity	49.66 ▓▓
	sex	49.47 ▓▓
	sex_ethnicity	48.82 ▓▓
recall	age	49.34 ▓▓
	ethnicity	50.65 ▓▓
	sex	50.95 ▓▓
	sex_ethnicity	50.16 ▓▓