Individual Consistency eXplorer (icx) Python Package
This package provides the functionality for an interactive Streamlit dashboard designed to support stakeholders in exploring individual fairness notions within algorithmic decision-making systems.
The dashboard allows users to:
- Explore and operate on a tabular dataset of individuals provided with their corresponding binary classifications;
- Define how similarity between individuals is measured, by configuring categorisation of attributes and how distances between attribute values are computed;
- Compute and visualise five individual fairness metrics that summarise the consistency of classifications across the dataset; and
- Inspect attributes of specific individuals and of those individuals most similar to them, to explore variations in attribute values and allow like-for-like comparisons of classifications.
This package implements the functionality described in a submission to ECAI Demo Track 2025, and further details and documentation will be provided upon publication.
To see an online version of the dashboard, see Individual Consistency eXplorer Online.
📦 Installation
It is recommended to install icx in a virtual environment (e.g., conda).
pip install icx
Basic Usage
from icx import dashboard
Run the dashboard
dashboard.run()
In the dashboard there is the ability to upload your own datasets or use the demo datasets provided.