These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
AI Bias Auditing Tool
AI Bias Auditing Tool is a Python tool for detecting potential bias in tabular datasets. It compares outcome rates across demographic groups and reports the disparities through a fairness metric, charts and a plain-language audit report. It is built with Pandas, Fairlearn, Matplotlib and ipywidgets, and runs in Google Colab. It is designed for beginners and for anyone who wants a quick first check for group-level disparities in decision data, such as loan approvals or hiring outcomes.
The user uploads a CSV dataset and picks two columns from interactive dropdown menus. The first is the sensitive attribute, such as gender, age or race. The second is the outcome column, such as Approved/Rejected or Yes/No. The tool converts the outcome into binary values and calculates the positive-outcome (approval) rate for each group. It then uses Fairlearn to compute the Demographic Parity Difference, which is the gap between the highest and lowest group approval rates.
The result is read against fixed thresholds. A value of 0.00 means perfect parity, below 0.05 means low disparity, 0.05 to 0.10 means moderate disparity, and above 0.10 means significant disparity. The tool produces two charts: approval rates by group, and positive and negative outcome counts by group. It also produces a bias assessment summarising the findings. It was tested on the German Credit Dataset. The developer plans to add more fairness metrics, PDF reports, a Streamlit web app, mitigation recommendations and model-level auditing.
Auto-discovered on 2026-10-28 by OECD Catalogue Automation
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