A review of taxonomies of explainable artificial intelligence (XAI) methods
T Speith - Proceedings of the 2022 ACM conference on fairness …, 2022 - dl.acm.org
The recent surge in publications related to explainable artificial intelligence (XAI) has led to
an almost insurmountable wall if one wants to get started or stay up to date with XAI. For this …
an almost insurmountable wall if one wants to get started or stay up to date with XAI. For this …
Requirements engineering for machine learning: A review and reflection
Today, many industrial processes are undergoing digital transformation, which often
requires the integration of well-understood domain models and state-of-the-art machine …
requires the integration of well-understood domain models and state-of-the-art machine …
[HTML][HTML] Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions
Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …
A new perspective on evaluation methods for explainable artificial intelligence (xai)
One of the big challenges in the field of explainable artificial intelligence (XAI) is how to
evaluate explainability approaches. Many evaluation methods (EMs) have been proposed …
evaluate explainability approaches. Many evaluation methods (EMs) have been proposed …
Explainable software systems: from requirements analysis to system evaluation
L Chazette, W Brunotte, T Speith - Requirements Engineering, 2022 - Springer
The growing complexity of software systems and the influence of software-supported
decisions in our society sparked the need for software that is transparent, accountable, and …
decisions in our society sparked the need for software that is transparent, accountable, and …
A means to what end? evaluating the explainability of software systems using goal-oriented heuristics
Explainability is an emerging quality aspect of software systems. Explanations offer a
solution approach for achieving a variety of quality goals, such as transparency and user …
solution approach for achieving a variety of quality goals, such as transparency and user …
A seven-layer model with checklists for standardising fairness assessment throughout the AI lifecycle
Problem statement: Standardisation of AI fairness rules and benchmarks is challenging
because AI fairness and other ethical requirements depend on multiple factors, such as …
because AI fairness and other ethical requirements depend on multiple factors, such as …
Quo vadis, explainability?–A research roadmap for explainability engineering
Abstract [Context and motivation] In our modern society, software systems are highly
integrated into our daily life. Quality aspects such as ethics, fairness, and transparency have …
integrated into our daily life. Quality aspects such as ethics, fairness, and transparency have …
Mapping the Potential of Explainable Artificial Intelligence (XAI) for Fairness Along the AI Lifecycle
The widespread use of artificial intelligence (AI) systems across various domains is
increasingly highlighting issues related to algorithmic fairness, especially in high-stakes …
increasingly highlighting issues related to algorithmic fairness, especially in high-stakes …
How to evaluate explainability?-a case for three criteria
T Speith - 2022 IEEE 30th International Requirements …, 2022 - ieeexplore.ieee.org
The increasing complexity of software systems and the influence of software-supported
decisions in our society have sparked the need for software that is safe, reliable, and fair …
decisions in our society have sparked the need for software that is safe, reliable, and fair …