-------- Forwarded Message -------- Subject: [AISWorld] Final CFP AMCIS2022 - Track: Artificial Intelligence and Semantic Technologies for Intelligent Information Systems Date: Mon, 28 Feb 2022 13:37:02 -0500 From: Vijayan Sugumaran sugumara@oakland.edu To: aisworld@lists.aisnet.org

CALL FOR PAPERS AMCIS 2021

Minneapolis, MN, August 10-14, 2022

Paper submission deadline: March 1 (5:00pm Eastern Standard Time, US)

Submission System Link: https://new.precisionconference.com/user/login

Track: AI and Semantic Technologies for Intelligent Information Systems

Sponsoring SIG: SIGODIS

Track Description:

The purpose of this track is to provide a forum for academics and practitioners to identify and explore the issues, opportunities, and solutions using Artificial Intelligence, computational ontologies, data driven IS, and intelligence related to business and systems including the social web, intelligent systems design, implementation, integration and deployment. An increasing number of artificial intelligence-based systems are being developed in different application domains employing a variety of tools and technologies. This track is intended to increase cross-fertilization of ideas from these areas, share lessons learned and stimulate areas for further research.

Best papers from this Track will be fast tracked for publication in a special issue of International Journal of Intelligent Information Technologies (http://www.idea-group.com/IJIIT).

Track Chair:

Don Heath, University of Wisconsin Oshkosh, drheath2@gmail.com mailto:drheath2@gmail.com

Mini-tracks:

Mini-Track I: Social, Ethical, & Practical Impacts of AI for Organizations and Individuals

AI is an important and increasingly pervasive tool of industry whose widespread adoption has given rise to several criticisms, such as lack of transparency of analytical models, lack of explainability of results, workforce disruption, and the potential to introduce or perpetuate implicit biases. The aim of this mini-track is to provide a forum for addressing the social, ethical, and practical aspects of AI and ML. Particularly, papers exploring the impact of AI/ML through various analytic lenses including societal, organizational, and individual perspectives are welcome.

Mini-Track Co-Chairs:

Vijayan Sugumaran, sugumara@oakland.edu mailto:sugumara@oakland.edu Stefan Kirn, stefan.kirn@uni-hohenheim.de mailto:stefan.kirn@uni-hohenheim.de

Mini-Track II: Innovative Technologies for Managing Data-intensive Systems

Evoked by recent trends, such as big data, data science or cloud computing, the planning and engineering of IS in today's data-driven world is getting progressively more complex. In many cases, sophisticated approaches are required to overcome the data-intensive nature of such endeavors. At this point, established technologies, as they have been used for many years, are reaching their limits. However, innovative technologies and concepts, such as artificial intelligence, automation, cloud computing, composable architectures, continuous integration, micro services, domain specific ontologies or decision support systems appear to be promising "enablers" to meet the current demands. To overcome their realization shortcomings, a plethora of facets must be handled. Hence, in this mini-track, we welcome a variety of research approaches including, but not limited to, theoretical articles, reviews and use case studies that are related to the use of innovative technologies for planning, engineering, deploying, testing and operating data-intensive systems.

Mini-Track Co-Chairs:

Matthias Volk, matthias.volk@ovgu.de mailto:matthias.volk@ovgu.de Daniel Staegemann, daniel.staegemann@ovgu.de mailto:daniel.staegemann@ovgu.de

Mini-Track III: Promises and Perils of Artificial Intelligence and Machine Learning: Disruption, Adoption, Dehumanisation, Governance, Risk and Compliance

In the last decade, Artificial Intelligence (AI) and Machine Learning (ML) have developed from peripheral technologies to dominant drivers of innovation. They are routinely used to recognize images; parse speech; respond to questions; make decisions; and replace humans. Given that AI and ML tools are becoming a part of our everyday lives, it is critical that researchers and practitioners understand their state of art, adoption and influence. Improperly deployed AI and ML tools can violate privacy, threaten safety, and take questionable decisions that can affect individuals, organizations and ultimately society. This minitrack will focus on the promises and perils of AI and ML with a particular focus on (a) adoption, (b) disruption, (c) potential dehumanisation, and (c) governance, risk, compliance and ethical mechanisms required to protect and enhance human wellbeing. We welcome wide-ranging papers with qualitative and quantitative orientations; with theoretical and practical contributions; from personal, organizational and societal perspectives.

Mini-Track Co-Chairs:

Valeria Sadovykh, valeriasadovykh@gmail.com mailto:valeriasadovykh@gmail.com David Sundaram, d.sundaram@auckland.ac.nz mailto:d.sundaram@auckland.ac.nz

Kevin Craig, kevin@kevincraig.net mailto:kevin@kevincraig.net

Mini-Track IV: Artificial Intelligence, Machine Learning, and Digital Transformation

Artificial Intelligence (AI) and Machine Learning (ML) are redefining businesses and accelerating digital transformation. The areas of artificial intelligence, machine learning, digital transformation, analytics, visualization, human-AI interaction, and a variety of AI and digital transformation topics have become critical to businesses as they navigate the pandemic, endemic, and new normal landscapes. The purpose of this mini-track is to provide a venue and forum for researchers involved in these bleeding-edge technologies to share research findings, explore new research directions, and build networks.

Mini-Track Co-Chairs:

John Erickson, johnerickson@unomaha.edu mailto:johnerickson@unomaha.edu Keng Siau, klsiau@cityu.edu.hk mailto:klsiau@cityu.edu.hk

Mini-Track V: Multi-modal Data Analytics for Intelligent Systems

In this era of data explosion, multi modal data from various sensors have been widely available to solve complex challenging problems in various application areas like automation industry, health care, logistics, smart city, transportation and many more. The solution to design artificial intelligent system has been feasible with the growth of intelligent techniques like deep learning, reinforcement learning which can address the different aspects of these challenges.

This mini track submission aims to bring cross-disciplinary original research and review articles with a focus on integrated concepts and technologies, insights from the multi-modal data, design of intelligent system and how to deal with these challenges under resources-constrained environments. The contribution can be new models, algorithms, innovative applications, but also practical solutions that particularly focus on how to apply generic techniques to specific applications.

Mini-Track Co-Chairs:

Amudha J., j_amudha@blr.amrita.edu mailto:j_amudha@blr.amrita.edu Tiago Falk, tiago.falk@inrs.ca mailto:tiago.falk@inrs.ca Supriya M., m_supriya@blr.amrita.edu mailto:m_supriya@blr.amrita.edu Rajakumar Arul, rajakumararul@ieee.org mailto:rajakumararul@ieee.org

Mini-Track VI: Intelligent Systems and Machine Learning- Solutions, Technologies and Techniques

The world of Artificial Intelligence and Machine Learning continues to accelerate at an unfathomable pace and made its foot print in almost all the fields. While artificial intelligence refers to the concept of creating intelligent machines that can mimic human cognitive abilities and behaviors, machine learning refers to a specific application of AI where machines can learn from data without being explicitly programmed. Intelligent systems are technologically superior machines that understand and react to their surroundings. Intelligent systems find their applications in a variety of fields, including factory automation, Assistive robotics, Military, Medical-care, Education, Intelligent-transportation etc. Machines have recently demonstrated the ability to learn and even master tasks that were previously thought to be extremely difficult for machines, demonstrating that machine learning algorithms are potentially useful elements of detection and decision support systems. However these intelligent systems have lots of potential research problems that need to be addressed in future.

Mini-Track Co-Chairs:

Annie Uthra R annieu@srmist.edu.in mailto:annieu@srmist.edu.in

Submission Information:

URL for submission: https://new.precisionconference.com/user/login URL for types of submission & instructions: https://amcis2022.aisconferences.org/submissions/types-of-submissions/

Important dates:

* January 21, 2022: Manuscript submissions begin * March 1, 2022: Completed research and ERF submissions are due at 5 p.m. EST * April 15, 2022: Workshop, panel, TREO and PDS submissions are due at 5 p.m. EST

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Vijayan Sugumaran, Ph.D.

Distinguished Professor, Management Information Systems

Chair, Department of Decision and Information Sciences

Co-Director, Center for Data Science and Big Data Analytics

School of Business Administration

Oakland University

Rochester, MI 48309

Phone: 248-370-4649

Fax: 248-370-4275

Email: sugumara@oakland.edu mailto:sugumara@oakland.edu ================================================

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