Explainability for Embedding AI: Aspirations and Actuality
Abstract
With artificial intelligence (AI) embedded in many everyday software systems, effectively and reliably developing and maintaining AI systems becomes an essential skill for software developers. However, the complexity inherent to AI poses new challenges. Explainable AI (XAI) may allow developers to understand better the systems they build, which, in turn, can help with tasks like debugging. In this paper, we report insights from a series of surveys with software developers that highlight that there is indeed an increased need for explanatory tools to support developers in creating AI systems. However, the feedback also indicates that existing XAI systems still fall short of this aspiration. Thus, we see an unmet need to provide developers with adequate support mechanisms to cope with this complexity so they can embed AI into high-quality software in the future.
Cite
@article{arxiv.2504.14631,
title = {Explainability for Embedding AI: Aspirations and Actuality},
author = {Thomas Weber},
journal= {arXiv preprint arXiv:2504.14631},
year = {2025}
}
Comments
Second Workshop on Engineering Interactive Systems Embedding AI Technologies at EICS 2024, Tuesday June 25th, 2024 - Cagliary, Sardinia, Italy