In recent years, Artificial Intelligence has become more and more relevant in
our society. Creating AI systems is almost always the prerogative of IT and AI
experts. However, users may need to create intelligent solutions tailored to
their specific needs. In this way, AI systems can be enhanced if new approaches
are devised to allow non-technical users to be directly involved in the
definition and personalization of AI technologies. End-User Development (EUD)
can provide a solution to these problems, allowing people to create, customize,
or adapt AI-based systems to their own needs. This paper presents a systematic
literature review that aims to shed the light on the current landscape of EUD
for AI systems, i.e., how users, even without skills in AI and/or programming,
can customize the AI behavior to their needs. This study also discusses the
current challenges of EUD for AI, the potential benefits, and the future
implications of integrating EUD into the overall AI development process.

本文介绍了一个系统性文献综述，旨在探讨当前 End-User Development 对于人工智能系统的景观，即如何使用户即使没有 AI 和 / 或编程技能，亦可定制 AI 行为以满足其需求。本研究还讨论了 EUD 面临的当前挑战，潜在的益处以及将 EUD 整合到整个 AI 开发过程中的未来影响。

人工智能的最终用户开发：一项系统文献综述

End-User Development for Artificial Intelligence: A Systematic  Literature Review

Many high-performance models suffer from a lack of interpretability. There
has been an increasing influx of work on explainable artificial intelligence
(XAI) in order to disentangle what is meant and expected by XAI. Nevertheless,
there is no general consensus on how to produce and judge explanations. In this
paper, we discuss why techniques integrating connectionist and symbolic
paradigms are the most efficient solutions to produce explanations for
non-technical users and we propose a reasoning model, based on definitions by
Doran et al. [2017] (arXiv:1710.00794) to explain a neural network's decision.
We use this explanation in order to correct bias in the network's decision
rationale. We accompany this model with an example of its potential use, based
on the image captioning method in Burns et al. [2018] (arXiv:1803.09797).

本文讨论了为非技术用户产生解释的最有效和高效的解决方案，提出了一种基于连接主义和符号范式的推理模型来解释神经网络的决策，并用一个例子演示了它的潜在用途。