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From: MISQ AI SI <[log in to unmask]>
Date: Tue, Jun 4, 2019 at 11:31 AM
Subject: [AISWorld] MISQ Special Issue "Managing AI" CFP
To: <[log in to unmask]>
Cc: Bin Gu <[log in to unmask]>, Nicholas Berente <[log in to unmask]>


MIS Quarterly Special Issue on “Managing AI”

Call for Papers



Submission Deadline:

Full papers due November 15, 2019



Artificial intelligence (“AI”) refers to machines performing the cognitive
functions typically associated with humans - including perceiving,
reasoning, learning, interacting, etc. AI is not confined to one or a few
applications, but rather is a pervasive economic, societal, and
organizational phenomenon. Examples of AI technologies include robotics and
autonomous vehicles, facial recognition, natural language processing,
virtual agents, and machine learning, which are being deployed in a variety
of problem domains ranging from cybersecurity to fintech to education to
healthcare. Technologies involving AI provide inestimable possibilities for
enhancing people’s lives in a variety of areas including their homes,
healthcare, education, employment, entertainment, safety, and
transportation.  Similarly, AI provides businesses with unprecedented
opportunities for designing intelligent products, devising novel service
offerings, and inventing new business models and organizational forms. But
AI is not a technological panacea. Accompanying the horizon of
possibilities are a host of emerging and complex challenges around business
strategies, human-AI interfaces, data, privacy, security, ethics, labor,
human rights, and national security. Today’s managers need to deal with
both possibilities and challenges that accompany widespread AI. This
special issue of *MIS Quarterly* focuses on understanding the management of
AI.  For details, see:

https://www.misq.org/skin/frontend/default/misq/pdf/CurrentCalls/ManagingAI.pdf



*MIS Quarterly*, and the information systems field more generally, has a
rich tradition of work dealing with the human and technical elements in
managing information systems. Whereas the transformative potential of AI is
widely recognized, there is significant uncertainty for businesses on how
to manage AI and its implications. The information systems field has
developed substantial knowledge on managing information technologies and
systems for different objectives, stakeholders, and levels of analysis.  To
what extent this knowledge translates to AI and to what extent AI falsifies
assumptions, raises new questions, and creates new opportunities remains an
open question that requires careful empirical and theoretical work. AI
presents a great opportunity to challenge how we think about managing
information systems and how we need to recalibrate that knowledge to manage
AI.



The special issue is looking for papers that meet four specific criteria:



1. Papers must distinguish fundamentally between AI and other forms of
digital technologies, and theorize on the specific differences.  Direct
applications of existing theory on IT and organization (without
differentiating AI from generic IT) are not suitable for the special issue.



2. Papers must focus on management practices of AI to enhance value or
mitigate harm in the development, implementation, management, use and/or
governance of AI.  We particularly encourage research on new forms of
management on the inter-actions between human resources, AI, and other
material resources.  As boundaries between human and machine become
increasingly blurry, we call for new thinking on management forms and
structures.



3. Papers must provide novel contributions to knowledge about the
management of AI.  Any form of rigorous theoretical contribution
(conceptual or empirical) using any scholarly method is welcome.  While we
welcome papers across diverse theoretical perspectives and research
methods, descriptive studies that summarize state of practice of AI
applications without a corresponding contribution to theory are not
suitable for the special issue.



4. Papers must consider both the social and technical aspect of AI.
Studies that focus only on the technical aspect of AIwithout placing
salience on management of AI do not correspond to the focus of the special
issue.  We encourage studies on management of AI at and across a variety of
levels of analysis, including organizations, institutions, platforms,
ecosystems, and societies.



Potential topics include, but are not limited to:

•Management, control, and governance of AI-related resources and
capabilities.

•Changes in strategy, structure, functions, workforce, alignment,
processes, and control that flow from management of AI.

•Managing intended and unintended AI-related outcomes across levels of
analysis.

•AI-enabled changes to business strategy, business models, and value
creation processes.

•Management of AI-fostered innovations, including digital product
development and software development.

•Managing  policy,  legislative,  ethical,  moral,  and  societal
implications  of  AI,  including  intellectual  property rights ownership.

•Data guardianship, security, and privacy in AI contexts.

•AI as management, in conjunction with humans or otherwise.

•Evaluation and monitoring of AI and associated organizational activity.

•Managing design issues associated with AI in infrastructure, artefacts,
products, platforms, ecosystems and markets.



Key Dates:

•First round submissions:  November 15, 2019

•Round 1 decisions:  February 15, 2020

•Workshop (location TBD):  March 20, 2020

•Second round submissions:  June 15, 2020

•Second round decisions to authors:  August 31, 2020

•Third and final round submissions:  November 15, 2020

•Third and final round decisions to authors:  December 31, 2020



Special Issue Editors:

Nicholas Berente, University of Notre Dame [log in to unmask]

Bin Gu, Arizona State University [log in to unmask]

Jan Recker, University of [log in to unmask]

Radhika Santhanam, University of Oklahoma [log in to unmask]



Special Issue Editorial Board:

Michael Barrett, Cambridge University

Roger Chiang, University of Cincinnati

Kevin Crowston, Syracuse University

Hailiang Chen, University of Hong Kong

Pei-Yu Chen, Arizona State

Brad Greenwood, University of Minnesota

Robert Gregory, University of Virginia

JJ Hsieh, Georgia State University

Peng Huang, University of Maryland

Ke-wei Huang, National University of Singapore

Zhengrui Jiang, Nanjing University

De Liu, University of Minnesota

Karthik Kannan, Purdue University

Deepa Mani, Indian School of Business

Jeff Nickerson, Stevens Institute of Technology

Brian Pentland, Michigan State University

Yuqing Ren, University of Minnesota

Lionel Robert, University of Michigan

Rajiv Sabherwal, University of Arkansas

Nachiketa Sahoo, Boston University

Stefan Seidel, University of Liechtenstein

Mari Clara Stein, Copenhagen Business School

Jingjing Zhang, Indiana University

Rong Zheng, Hong Kong University of Science and Technology
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