10 citations · 10 across the 4 of their papers we have counts for
4 papers
This part looks alike this: identifying important parts of explained instances and prototypes
Jacek Karolczak, Jerzy Stefanowski
Although prototype-based explanations provide a human-understandable way of representing model predictions they often fail to direct user attention to the most relevant features. W…
DetoxAI: a Python Toolkit for Debiasing Deep Learning Models in Computer Vision
Ignacy Stępka, Lukasz Sztukiewicz, Michał Wiliński +1
While machine learning fairness has made significant progress in recent years, most existing solutions focus on tabular data and are poorly suited for vision-based classification t…
Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps
Lukasz Sztukiewicz, Ignacy Stępka, Michał Wiliński +1
The widespread adoption of machine learning systems has raised critical concerns about fairness and bias, making mitigating harmful biases essential for AI development. In this pap…
Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues
Riccardo Albertoni, Sara Colantonio, Piotr Skrzypczyński +1
Reproducibility is one of the core dimensions that concur to deliver Trustworthy Artificial Intelligence. Broadly speaking, reproducibility can be defined as the possibility to rep…