The `AI Bias Auditor` script is a vital tool designed to evaluate Machine Learning (ML) models for potential bias against protected features such as gender, race, or other sensitive attributes. By generating detailed fairness reports, it identifies disparities in model outcomes to ensure ethical, fair, and inclusive AI practices.

The corresponding `ai_bias_auditor.html` file complements this script by offering a user-friendly guide outlining its purpose, procedures, and usage tips. Together, these resources serve developers, ML practitioners, and organizations striving for fairness in their AI pipelines.

• Introduction
• Purpose
• Key Features
• How the Bias Auditor Works
• Dependencies
• Usage
• Understanding the Bias Report
• Best Practices
• Role in the G.O.D. Framework
• Future Enhancements

Bias in machine learning models can result in unfair treatment of individuals or groups based on sensitive attributes. The `ai_bias_auditor.py` script provides a systematic way to detect, quantify, and report bias related to protected features, enabling organizations to mitigate such issues and align their AI solutions with ethical standards.

This script evaluates disparities in model outcomes by analyzing group-level statistics, calculating fairness gaps, and flagging potential biases based on configurable thresholds.

The main goals of this script are:

• Detect and Quantify Bias: Identify disparities in outcome metrics for groups categorized by sensitive features like gender or race.
• Promote Ethical AI Development: Help organizations build fair models by uncovering biases in predictions or outcomes.
• Provide Actionable Insights: Generate detailed reports that quantify fairness gaps to inform remediation strategies.
• Enhance Trust and Accountability: Allow stakeholders to audit and validate the fairness of their ML models.

This script includes several features that make it robust and versatile:

• Bias Evaluation for Protected Features: Audit attributes such as gender, race, or other sensitive features against outcome metrics.
• Fairness Gap Quantification: Calculate group-based metrics and evaluate fairness gaps to quantify disparity between the best- and worst-performing groups.
• Customizable Bias Thresholds: Define thresholds (e.g., fairness gap > 0.1) to flag significant bias.
• Detailed Reports: Provide insights into the performance of each protected group, including average outcomes (`group_stats`) and fairness gap analysis.
• Lightweight Implementation: Designed for easy integration into existing ML pipelines with minimal dependencies.

The script operates by analyzing the relationship between protected features (e.g., gender, race) and an outcome feature (e.g., prediction accuracy). Below is a step-by-step breakdown:

1. Initialization:

1. The `BiasAuditor` is initialized with:
   1. A list of protected features to monitor (e.g., `[“gender”, “race”]`).
   2. The target outcome feature to evaluate (e.g., `prediction_accuracy`).

2. Data Analysis:

1. For each protected feature:
   1. Group-level statistics (mean outcome per group) are calculated.
   2. The fairness gap (difference between highest and lowest group mean) is computed.
   3. The script flags whether the disparity exceeds a predefined bias threshold (e.g., 0.1).

3. Bias Report Generation:

1. A comprehensive report is created, detailing:
   1. Group statistics (`group_stats`): Average outcomes for each subgroup.
   2. Fairness gap: The difference between the best-performing and worst-performing groups.
   3. Bias flag: Whether the fairness gap exceeds the threshold (`is_biased`).

The script evaluates bias using the following code: ```python def evaluate_bias(self, data):

  """
  Analyzes dataset outcomes for bias related to protected features.
  :param data: Dataset (pandas DataFrame)
  :return: Bias report
  """
  report = {}
  for feature in self.protected_features:
      group_stats = data.groupby(feature)[self.outcome_feature].mean()
      fairness_gap = group_stats.max() - group_stats.min()

      report[feature] = {
          "group_stats": group_stats.to_dict(),
          "fairness_gap": fairness_gap,
          "is_biased": fairness_gap > 0.1  # Define threshold for significant bias
      }
  return report

```

This guarantees a detailed summary of how each sensitive feature impacts the fairness within model outcomes.

The script depends on a lightweight set of Python libraries, ensuring ease of installation and integration.

• `pandas`: Used to handle data structures and compute group-level statistics.
• `logging`: (Optional) Can be enabled to log analysis steps for debugging or record-keeping.

To ensure the required library is installed, run: