Artificial Intelligence (AI) has rapidly emerged as a key disruptive technology in the 21st century. At the heart of modern AI lies Deep Learning (DL), an emerging class of algorithms that has enabled today's platforms and organizations to operate at unprecedented efficiency, effectiveness, and scale. Despite significant interest, IS contributions in DL have been limited, which we argue is in part due to issues with defining, positioning, and conducting DL research. Recognizing the tremendous opportunity here for the IS community, this work clarifies, streamlines, and presents approaches for IS scholars to make timely and high-impact contributions. Related to this broader goal, this paper makes five timely contributions. First, we systematically summarize the major components of DL in a novel Deep Learning for Information Systems Research (DL-ISR) schematic that illustrates how technical DL processes are driven by key factors from an application environment. Second, we present a novel Knowledge Contribution Framework (KCF) to help IS scholars position their DL contributions for maximum impact. Third, we provide ten guidelines to help IS scholars generate rigorous and relevant DL-ISR in a systematic, high-quality fashion. Fourth, we present a review of prevailing journal and conference venues to examine how IS scholars have leveraged DL for various research inquiries. Finally, we provide a unique perspective on how IS scholars can formulate DL-ISR inquiries by carefully considering the interplay of business function(s), application areas(s), and the KCF. This perspective intentionally emphasizes inter-disciplinary, intra-disciplinary, and cross-IS tradition perspectives. Taken together, these contributions provide IS scholars a timely framework to advance the scale, scope, and impact of deep learning research.

翻译：人工智能(AI)在21世纪迅速成为一个关键的破坏性技术。现代人工智能的核心是深度学习(DL),这是一个新兴的算法类别,它使今天的平台和组织能够以前所未有的效率、有效性和规模运作。尽管人们对此兴趣很大,但对DL的IS贡献有限,我们说,这在一定程度上是由于定义、定位和进行DL研究方面的问题。认识到这里对IS界来说是一个巨大的机会,这项工作澄清、精简和提出了让IS学者作出及时和高影响贡献的方法。与这一更广泛的目标有关,本文件作出了五种及时的贡献。首先,我们系统地总结了DL的主要组成部分,在新的信息系统研究深度学习(DL-ISR)中,我们系统地总结了DL的主要组成部分,说明了技术达尔进程是如何受应用环境关键因素驱动的。第二,我们提出了一个新的知识贡献框架(KCF),帮助IS学者定位其DL贡献的最大影响。第三,我们提供了十项准则,帮助IS学者以系统、高品质的方式产生严格和相关的DL-IS(DIS),我们从历史、历史范围和历史研究领域,我们用一个独特的研究范围来审查当前IS-L的研究领域,我们如何利用S-I研究。