Awesome

<h1 align="center"> <img width="250" src="https://www.dropbox.com/s/7xo4w8ak9lkwrqy/fairsight-logo.png?raw=1" alt="FairSight"> </h1> FairSight is a viable fair decision making system to assist decision makers in achieving fair decision making through the machine learning workflow.

Reference

Ahn, Y., & Lin, Y. R. (2019). Fairsight: Visual analytics for fairness in decision making. IEEE transactions on visualization and computer graphics (TVCG), 26(1), 1086-1095.

Specification

React: Frontend framework for rendering and communicating with data
django: Python-based backend framework for serving API of data and running machine learning work
scss: The stylesheet grammar for more flexible structure
d3.js: Javascript-based visualization library

FairDM

<h1 align="center"> <img width="80%" src="https://www.dropbox.com/s/kkh3qk5iabp5o92/fairdm-teaser.png?raw=1" alt="FairSight"> </h1> FairSight is developed on top of FairDM, a general fair decision making framework. Our framework is a model-agnostic framework with its goal to provide a fairness pipeline to guide the examination of fairness at each step (from input to output) in the workflow.

System

<h1 align="center"> <img width="700" src="https://www.dropbox.com/s/kg0fo0jawm954q3/fairsight-system.png?raw=1" alt="FairSight"> </h1> (a) Generator: The workflow of FairSight starts with setting up inputs including the sensitive attribute and protected group. (b) Ranking View: After running a model, the ranking outcome and measures are represented. (c) Global Inspection View: Visualizes the two spaces and the mapping process of Individual and Group fairness provided in the separate tap. (d) Local Inspection View: When an individual is hovered, Local Inspection View provides the instance- and group-level exploration. (e) Feature Inspection View: Users can investigate the feature distortion and feature perturbation to identify features as the possible source of bias. (f) Ranking List View: All generated ranking outcomes are listed and plotted.