Engineering successful machine learning (ML)-enabled systems poses various challenges
from both a theoretical and a practical side. Among those challenges are how to effectively …

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in
requirements engineering for artificial intelligence (RE4AI), eg, the difficulty in specifying and …

Integrating ethical practices into the AI development process for artificial intelligence (AI) is
essential to ensure safe, fair, and responsible operation. AI ethics involves applying ethical …

Driving automation systems, including autonomous driving and advanced driver assistance,
are an important safety-critical domain. Such systems often incorporate perception systems …

While the operationalisation of high-level AI ethics principles into practical AI/ML systems
has made progress, there is still a theory-practice gap in managing tensions between the …

Human involvement in machine learning (ML) is a promising paradigm to overcome the
limitations of purely automated predictions and improve the applicability of ML. We compile …

Objective This paper aims to investigate the characteristics of real quality issues in MLSSs
from the viewpoint of practitioners. This empirical study aims to identify a catalog of quality …

Abstract [Context and Motivation] Many recent studies highlight explainability as an
important requirement that supports in building transparent, trustworthy, and responsible AI …

Abstract As Artificial Intelligence makes astonishing progress, various AI-embedded
applications are being built to unleash their potential. However, all technologies come with …

By incorporating usability into the creation or evaluation of user interface designs, it is
possible to provide experiences that are effective, efficient, and satisfying interactions for …