Mapping the Potential of Explainable Artificial Intelligence (XAI) for Fairness Along the AI Lifecycle

no code implementations • 29 Apr 2024 • Luca Deck, Astrid Schomäcker, Timo Speith, Jakob Schöffer, Lena Kästner, Niklas Kühl

The widespread use of artificial intelligence (AI) systems across various domains is increasingly highlighting issues related to algorithmic fairness, especially in high-stakes scenarios.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1

Sources of Opacity in Computer Systems: Towards a Comprehensive Taxonomy

no code implementations • 26 Jul 2023 • Sara Mann, Barnaby Crook, Lena Kästner, Astrid Schomäcker, Timo Speith

The taxonomy provides a starting point for requirements engineers and other practitioners to understand contextually prevalent sources of opacity, and to select or develop appropriate strategies for overcoming them.

Fairness

What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research

no code implementations • 15 Feb 2021 • Markus Langer, Daniel Oster, Timo Speith, Holger Hermanns, Lena Kästner, Eva Schmidt, Andreas Sesing, Kevin Baum

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these stakeholders' desiderata) in a variety of contexts.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)