Metadata-Version: 2.4
Name: icx
Version: 0.0.4
Summary: This package provides the functionality for an interactive Streamlit dashboard designed to support stakeholders in exploring individual fairness notions within algorithmic decision-making systems. 
Project-URL: Homepage, https://github.com/maddiewaller/Individual-Consistency-eXplorer/
Author-email: Madeleine Waller <madeleine.waller@kcl.ac.uk>
License-Expression: MIT
License-File: LICENSE
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.0
Requires-Dist: gower
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: streamlit
Requires-Dist: streamlit-aggrid
Requires-Dist: streamlit-sortables
Description-Content-Type: text/markdown

# 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.


## 📦 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()
```
