5 research outputs found
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research
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. However, the
literature on XAI is vast, spreads out across multiple largely disconnected
disciplines, and it often remains unclear how explainability approaches are
supposed to achieve the goal of satisfying stakeholders' desiderata. This paper
discusses the main classes of stakeholders calling for explainability of
artificial systems and reviews their desiderata. We provide a model that
explicitly spells out the main concepts and relations necessary to consider and
investigate when evaluating, adjusting, choosing, and developing explainability
approaches that aim to satisfy stakeholders' desiderata. This model can serve
researchers from the variety of different disciplines involved in XAI as a
common ground. It emphasizes where there is interdisciplinary potential in the
evaluation and the development of explainability approaches.Comment: 57 pages, 2 figures, 1 table, to be published in Artificial
Intelligence, Markus Langer, Daniel Oster and Timo Speith share
first-authorship of this pape
Domänen parallele Maschinen
A computational model is introduced, which abstracts and idealizes computers with access to fragment shaders. While the set of functions computable by this model remains the same, the running times can be drastically reduced through parallelization compared to conventional models. Some of the algorithms designed for the model can be approximated using fragment shaders. With an automatic transcompilation scheme, fragment shader programs can be generated automatically from a description in a high-level language.In dieser Arbeit wird ein Rechenmodell, das Computer mit Zugriff zu Fragment Shader abstrahiert und idealisiert, eingeführt. Zwar bleibt der Umfang der durch dieses Modell berechenbarer Funktionen gleich, jedoch können die Laufzeiten durch Parallelisierung im Vergleich zu herkömmlichen Modellen drastisch verkürzt werden. Einige der für das Modell entworfenen Algorithmen lassen sich mithilfe von Fragment Shadern approximieren. In einer Hochsprache beschriebene Algorithmen werden automatisiert in Fragment Shader Programme übersetzt
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum