  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.
