As systems based on opaque Artificial Intelligence (AI) continue to flourish
in diverse real-world applications, understanding these black box models has
become paramount. In response, Explainable AI (XAI) has emerged as a field of
research with practical and ethical benefits across various domains. This paper
not only highlights the advancements in XAI and its application in real-world
scenarios but also addresses the ongoing challenges within XAI, emphasizing the
need for broader perspectives and collaborative efforts. We bring together
experts from diverse fields to identify open problems, striving to synchronize
research agendas and accelerate XAI in practical applications. By fostering
collaborative discussion and interdisciplinary cooperation, we aim to propel
XAI forward, contributing to its continued success. Our goal is to put forward
a comprehensive proposal for advancing XAI. To achieve this goal, we present a
manifesto of 27 open problems categorized into nine categories. These
challenges encapsulate the complexities and nuances of XAI and offer a road map
for future research. For each problem, we provide promising research directions
in the hope of harnessing the collective intelligence of interested
stakeholders.

通过提出 27 个开放问题分类为九类，本文不仅强调了 XAI 及其在现实世界中的应用的进展，也解决了 XAI 中的挑战，并强调了更广泛的视角和协作努力的需求，以推动 XAI 的发展和贡献于其持续成功。

可解释人工智能（XAI）2.0：开放挑战和跨学科研究方向的宣言

Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open  Challenges and Interdisciplinary Research Directions

One way of carving up the broad "AI ethics and society" research space that
has emerged in recent years is to distinguish between "near-term" and
"long-term" research. While such ways of breaking down the research space can
be useful, we put forward several concerns about the near/long-term distinction
gaining too much prominence in how research questions and priorities are
framed. We highlight some ambiguities and inconsistencies in how the
distinction is used, and argue that while there are differing priorities within
this broad research community, these differences are not well-captured by the
near/long-term distinction. We unpack the near/long-term distinction into four
different dimensions, and propose some ways that researchers can communicate
more clearly about their work and priorities using these dimensions. We suggest
that moving towards a more nuanced conversation about research priorities can
help establish new opportunities for collaboration, aid the development of more
consistent and coherent research agendas, and enable identification of
previously neglected research areas.

区分 “近期” 和 “长期” 研究在人工智能伦理和社会研究领域中已经具有广泛应用和重要性，但是文章对该分类方式的模棱两可和不一致性提出了一些担忧。为了建立新的合作机会、发展更一致的研究议程并确定以前忽视的研究领域，作者建议通过 4 个维度进行更加清晰的交流。