EquiAudit — Dataset Quality & Fairness Evaluation System

An AI-agent pipeline for evaluating datasets on data quality and fairness concerns, with an interactive Streamlit GUI, a headless CLI, and a Python API.

A dataset is fed into EquiAudit, which coordinates AI agents under optional user supervision to produce an audit report and a bias-mitigated dataset.

How It Works

AI agents interact bidirectionally with the analyst, invoke tools to query and compute statistics on the dataset, and synthesise findings into an audit report.

Each selected mitigation technique produces a transformed dataset variant; tools recompute fairness statistics on every variant; the AI agent compares them against the original statistics and produces a comparative mitigation report.

Demo

A video demonstration of the full GUI workflow is available on YouTube: https://youtu.be/_USTmBhzDkI

Documentation

Installation

From PyPI (recommended):

pip install equiaudit            # core pipeline
pip install "equiaudit[gui]"     # optional: Streamlit GUI

From source:

git clone https://github.com/pchmelo/EquiAudit.git
cd EquiAudit
pip install -r requirements.txt          # core pipeline
pip install -r requirements-gui.txt      # optional: Streamlit GUI

Configuration

Copy examples/config.yml and set your API keys as environment variables or in a .env file at the project root:

GOOGLE_API_KEY=your-google-gemini-api-key-here
OPENROUTER_API_KEY=your-openrouter-api-key-here

At least one cloud API key is required unless running a local model via Ollama (ollama serve).

See USAGE.md — Configuration File for the full annotated config reference.

Running the Application

The recommended entry point is examples/example_usage.py. Set mode: gui or mode: quick in examples/config.yml, then run:

python examples/example_usage.py

The script dispatches to the GUI or the headless evaluator based on the config:

# examples/example_usage.py
import os, yaml
from dotenv import load_dotenv
load_dotenv()

CONFIG_PATH  = os.path.join(os.path.dirname(__file__), "config.yml")
DATASET_PATH = os.path.join(os.path.dirname(__file__), "adult-all.csv")

with open(CONFIG_PATH, encoding="utf-8") as f:
    _mode = (yaml.safe_load(f) or {}).get("mode", "quick")

if _mode == "gui":
    from equiaudit.gui import launch
    launch(config_path=CONFIG_PATH, dataset_path=DATASET_PATH)
else:
    from equiaudit.cli import FairnessEvaluator
    evaluator = FairnessEvaluator(config_path=CONFIG_PATH)
    result = evaluator.evaluate(
        data=DATASET_PATH,
        # target="Income",                                          # overrides target_column in config
        # sensitive_columns=["Sex", "Race", "Age"],                # overrides sensitive_attribute_analysis
        # sensitive_pairs=[["Sex", "Race"], ["Age", "Education"]], # overrides pair_evaluation
        # mitigation_techniques=["reweighting", "smote"],          # overrides mitigation_techniques
    )
    if result.success:
        print(f"Report: {result.report_dir}")
    else:
        print(f"Error:  {result.error}")

See USAGE.md — Execution Modes for the full CLI flag reference and Python API usage.

Datasets

Example datasets are provided in src/equiaudit/data/ for testing. The default example is adult-all.csv (UCI Adult Census Income, target column Income).

Reports

Generated reports are saved under reports/<dataset>_<timestamp>/ and include a Markdown narrative, a PDF, per-group fairness metrics, and raw JSON stage data. A sample report is available in reports/.

GUI Screenshots

Framework configuration (dataset selection, model backend, target column, and pipeline options).

Sensitive attribute identification (agent-generated candidate list pending analyst confirmation).

Proxy model fairness results (per-group metric bar charts from the outcome disparity stage).

Previous results browser (inspect and compare past audit runs).