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

Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions

no code implementations • 30 Oct 2023 • Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith, Simone Stumpf

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

A New Perspective on Evaluation Methods for Explainable Artificial Intelligence (XAI)

no code implementations • 26 Jul 2023 • Timo Speith, Markus Langer

If meeting the requirement of explainability entails a reduction in system performance, then careful consideration must be given to which of these quality aspects takes precedence and how to compromise between them.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

Revisiting the Performance-Explainability Trade-Off in Explainable Artificial Intelligence (XAI)

no code implementations • 26 Jul 2023 • Barnaby Crook, Maximilian Schlüter, Timo Speith

If meeting the requirement of explainability entails a reduction in system performance, then careful consideration must be given to which of these quality aspects takes precedence and how to compromise between them.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

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)

Towards a Framework Combining Machine Ethics and Machine Explainability

no code implementations • 3 Jan 2019 • Kevin Baum, Holger Hermanns, Timo Speith

In this paper, we try to motivate and work towards a framework combining Machine Ethics and Machine Explainability.

Decision Making Ethics