Skip to main content

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

Project description

fairground

CI

"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")

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fairground-0.0.0a11.tar.gz (536.6 kB view details)

Uploaded Source

Built Distribution

fairground-0.0.0a11-py3-none-any.whl (542.1 kB view details)

Uploaded Python 3

File details

Details for the file fairground-0.0.0a11.tar.gz.

File metadata

  • Download URL: fairground-0.0.0a11.tar.gz
  • Upload date:
  • Size: 536.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for fairground-0.0.0a11.tar.gz
Algorithm Hash digest
SHA256 40888cb21fc896d629149c458e2c4e0c16a2ec24d63b3285d9a67d4c05ac5792
MD5 5519fb4c85b90c03ab46b7b3a7557b83
BLAKE2b-256 d3b966f9e44f19cd162a62a68ab49e4fdee99e79e6a85c7800d7a8732b61b483

See more details on using hashes here.

Provenance

The following attestation bundles were made for fairground-0.0.0a11.tar.gz:

Publisher: main.yaml on CoefficientSystems/fairground

Attestations:

File details

Details for the file fairground-0.0.0a11-py3-none-any.whl.

File metadata

File hashes

Hashes for fairground-0.0.0a11-py3-none-any.whl
Algorithm Hash digest
SHA256 7b544cb5ee2f1e8ce8661b3935634fc4990d0e2d891e5f4a22c678465d499e61
MD5 06084850916b5a9adcda814b055b0ffc
BLAKE2b-256 ff5fccf41e34ad5a0f2cdff346e4cb9ea2f266809bcbe05798878ef06e7877f7

See more details on using hashes here.

Provenance

The following attestation bundles were made for fairground-0.0.0a11-py3-none-any.whl:

Publisher: main.yaml on CoefficientSystems/fairground

Attestations:

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page