fairground

"There is nothing that surpasses the joy of creation, if only because through it one wins hours of self-forgetfulness, when one lives in a world of sound." – Clara Schuman

Compliance Tools for statistical analysis and debiasing for LLM powered applicant tracking systems.

Project cheatsheet

• pre-commit: pre-commit run --all-files
• pytest: pytest or pytest -s
• coverage: coverage run -m pytest or coverage html
• poetry sync: poetry install --sync
• updating requirements: see docs/updating_requirements.md

Initial project setup

1. See docs/getting_started.md or docs/quickstart.md for how to get up & running.
2. Check docs/project_specific_setup.md for project specific setup.
3. See docs/using_poetry.md for how to update Python requirements using Poetry.
4. See docs/detect_secrets.md for more on creating a .secrets.baseline file using detect-secrets.

fairground Statistical Analysis Utils

fairground provides a comprehensive suite of visuaisation and statistical analysis tools for assessing fairness and bias in selection processes, particularly useful for applicant tracking systems and hiring decisions.

Core Features

Selection Rate Analysis

The selection rate analysis tools help visualise and analyse how different groups are selected within your process:

• plot_selection_rate(): Creates a horizontal bar plot showing selection rates across selected protected characteristics groups
• plot_job_selection_rate(): Generates faceted plots to compare selection rates across different job roles

Parity Analysis

The parity analysis functions help identify and visualize disparities between groups:

• parity_difference(): Calculates the arithmetic difference in selection rates between groups
• plot_parity_difference(): Creates a multi-panel visualisation showing parity differences using each group as a baseline
• plot_parity_difference_scatter(): Generates a scatter plot heatmap showing parity differences between all group combinations

Disparate Impact Analysis

Tools for analysing disparate impact and selection rate deviations:

• disparate_impact_ratio(): Calculates the ratio of selection rates between groups (also known as the adverse impact ratio)
• plot_selection_rate_deviation_with_disparate_impact_ratio_value(): Creates a comprehensive visualisation showing selection rate deviations from equal treatment and disparate impact ratio thresholds

Key Features

• Flexible Group Analysis: Support for both single and multiple demographic group analysis
• Regulatory Compliance: Built-in support for standard fairness metrics like the 4/5ths rule (disparate impact ratio)
• Visual Insights: Rich visualizations with detailed annotations including: -- Group sizes (n values) -- Statistical significance indicators -- Disparate impact ratio values -- Equal treatment thresholds

Example Usage

import pandas as pd
from fairground.utils import plot_selection_rate, plot_parity_difference

# Load your data
df = pd.read_csv("hiring_data.csv")

# Plot selection rates by gender
plot_selection_rate(df, "Gender", "Selected")

# Analyze parity differences across multiple demographics
plot_parity_difference(df, ["Gender", "Race", "Age"], "Selected")