Orange Blog

By: Žan Mervič, Sep 19, 2023

Why Removing Features Isn't Enough

In this blog, we confront the common misconception that merely removing a protected attribute from a dataset eliminates bias in model predictions. Our case study reveals that models trained without these attributes still produce biased results. This is due to feature correlations that indirectly capture the protected information. Our conclusion? You cannot sidestep the need for specialized fairness algorithms.

Categories: fairness

By: Žan Mervič, Sep 19, 2023

Orange Fairness - Reweighing as a preprocessor

Diving deeper into the Orange fairness Reweighing widget, we explore its use as a preprocessor for models. Discover the new widgets and fairness scoring metrics; all illustrated using the German credit dataset, supplemented with visual insights through box plots.

Categories: fairness reweighing

By: Žan Mervič, Sep 19, 2023

Orange Fairness - Reweighing a Dataset

Building on our exploration of the Orange fairness addon, this blog delves into the Reweighing widget. By adjusting weights for dataset instances, the widget addresses bias, focusing on underrepresented groups. Using the Compas dataset as an example, we demonstrate how bias decreases post-reweighting, presenting visual insights into the distribution of adjusted weights and their impact on dataset fairness.

Categories: fairness reweighing

By: Žan Mervič, Sep 19, 2023

Orange Fairness - Equal Odds Postprocessing

In this blog, we delve into the Equal Odds Postprocessing widget, a tool designed to enhance fairness in machine learning models. We break down how the algorithm works by modifying predictions to meet Equalized Odds criteria. Using a real-world example with the German credit dataset, we demonstrate its efficacy in improving fairness metrics while marginally affecting accuracy.

By: Žan Mervič, Sep 19, 2023

Orange Fairness - Adversarial Debiasing

This blog post focuses on the Adversarial Debiasing model in Orange, a tool for enhancing fairness in your machine learning algorithms. We will walk through how to use it and explain the trade-offs that come with using fairness algorithms.

By: Žan Mervič, Sep 18, 2023

Orange Fairness - Dataset Bias

In an era where AI drives decisions impacting real lives, fairness in machine learning is paramount. Take the `Adult` dataset, which shows discrepancies in salary predictions based on gender. Addressing such concerns, Orange introduces a fairness add-on. Using new widgets, users can identify and mitigate biases in their datasets or model predictions.

By: Noah Novšak, Aug 31, 2023

A dash of Dask

Updates in the dask project, integrating machine learning methods and enhancing efficiency in a data preprocessor.

Categories: dask development

By: Anja Mejač, Janez Demšar, May 26, 2023

Pumice challenge

One thousand Slovenian students took part in a data mining challenge

Categories: education

By: Janez Demšar, Apr 24, 2023

Živjo, Orange!

Orange speaks Slovenian!

Categories: gui

By: Janez Demšar, Mar 10, 2023

Interval Sliders

What does it take to add a second handle to a slider.

Categories: development