Our track is concerned with research on the design and evaluation of sociotechnical AI-based systems that achieve multi-sided outcomes and are meaningful to the businesses and/or society as well as the use and consequences of sociotechnical AI systems. Particularly, we welcome studies that examine impactful “AI design principles” and research that yields new principles for the design and evaluation of AI tools. This also includes studies that take on a hybridization approach and present designs of interesting human-AI hybrids in different contexts. We seek studies that are theoretical, empirical, or, technical, quantitative or qualitative, as long as the research yields impactful new insights. By “impactful,” we mean that the design principles should pivot from the existing research, theory, and practice; specifically, authors will need to demonstrate through their understanding of the existing opus, how their research builds on current knowledge on the topic, and not simply state that fact. By “multi-sided outcomes,” we mean an effective AI-based tool or system which achieves value not just for the developer or the corporation using it on consumers, employees or contributors but for those other people as well. The value should be in ways that go far beyond simple recommendations for movie choices, but improve people’s lives in fundamental ways (e.g., closing the income inequality gap, dampening systemic racial biases, reducing information silos, engaging with the challenges of global warming, improving the safety of seniors’ homes, etc.). By “sociotechnical AI,” we mean that studies should seek to open and provide insights into the black box of the user, the ecosystem of use and development, and the technology around it. For example, discussions of the AI tools should include the data and algorithms they are built on, how users are enticed into engaging with these tools, and what is the ecosystem that institutionalizes the tool-usage patterns that harm or foster the multi-sided outcomes. Through our interest in human-AI hybrids, we are also seeking studies that not only focus on the design of AI-based tools for the user but shed more light on how humans and AI can work collaboratively on a task. We invite studies that a) work on all levels of analysis, from the individual up to the societal, and b) research that unpacks not only the black box “of the user, the ecosystem of use and development, and the tool” but also of the interactions between user and tool, which may be at a more granular level. We also welcome submissions providing in-depth cases of implementation and use of AI in specific organizations and identifying its (unintended) consequences. We also ask that studies clearly delineate AI and AI-based tools from traditional information technologies and make a case for why AI (and its surrounding context) should be viewed differently.

Track Co-Chairs
Michael Chau, Ph.D., The University of Hong Kong
Hila Lifshitz-Assaf, Ph.D., New York University(NYU)
Alexander Maedche, Ph.D., Karlsruhe Institute of Technology (KIT)
Schedule

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2022
Monday, December 12th
12:00 AM

A Strategic Analysis of Algorithm Manipulation: a Lending Game perspective

Jiali ZHOU, HKUST
Jiexin ZHENG, hkust

12:00 AM

Algorithmic Decision-Making Systems: A Conceptualization and Agenda for Green IS Research

Antoine Grenier Ouimet, Smith School of Business at Queen's University
Shamel Addas, Queen's University

12:00 AM

An Intelligent Customization Framework for Tourist Trip Design Problems

Le Wang, Xi’an Jiaotong University
Xi Zhao, Xi'an Jiaotong University

12:00 AM

Characteristics of Contemporary Artificial Intelligence Technologies and Implications for IS Research

Benjamin van Giffen, University of St. Gallen
Nadine Barth, Institute of Information Management
André Sagodi, Institute of Information Management

12:00 AM

Conceptual Foundations on Debiasing for Machine Learning-Based Software

Anuschka Schmitt, University of St. Gallen
Maximilian Walser, University of St.Gallen
Tobias Benjamin Fahse, University of St.Gallen

12:00 AM

Credit Risk Modeling without Sensitive Features: An Adversarial Deep Learning Model for Fairness and Profit

Xiyang Hu, Carnegie Mellon University
Yan Huang, Carnegie Mellon University
Beibei Li, Carnegie Mellon University
Tian Lu, Arizona State University

12:00 AM

Development of an Automated Physician Review Classification System: A hybrid Machine Learning Approach

Sagarika Suresh THIMMANAYAKANAPALYA, university at buffalo
Pavankumar Mulgund, University at Buffalo
Raj Sharman, University at Buffalo, SUNY

12:00 AM

Fair Algorithms in Organizations: A Performative-Sensemaking Model

Manos Gkeredakis, University of Navarra

12:00 AM

Future imperfect: How AI developers imagine the future

Emmanuelle Vaast, McGill University

12:00 AM

Leveraging an Ecosystem for the Development of AI Applications

Xuetao Wang, The University of Sydney
Cheuk Hang Au, National Chung Cheng University
Evelyn Ng, The University of Sydney
Barney Tan, The University of New South Wales

12:00 AM

Machine Learning for ARUP: Time to Redefine the Ground Truth

Sergey Stroppiana Tabankov, University of Warwick

12:00 AM

Managing Organizational Identity Challenges Caused By AI Implementation: The Role Of AI Principles

Alexandra Haimerl, University of Passau
Anne-Sophie Mayer, Vrije Universiteit Amsterdam
Marina Fiedler, University of Passau

12:00 AM

Opening the Black-Box of AI: Challenging Pattern Robustness and Improving Theorizing through Explainable AI Methods

Dominik Stoffels, University of Passau
Stefan Faltermaier, University of Passau
Kim Simon Strunk, University of Passau
Marina Fiedler, University of Passau

12:00 AM

Restoring Justice: The Moderating Role of AI Agent in Consumers’ Reactions to Service Recovery

Yue Cheng, Peking University
Lingyun Qiu, Peking University
Chee-Wee Tan, Copenhagen Business School

12:00 AM

Review of Research on Human Trust in Artificial Intelligence

Yonggang Li, Information Systems and Analytics, National University of Singapore
Jungpil Hahn, National University of Singapore

12:00 AM

The Impact of Conversational Assistance on the Effective Use of Forecasting Support Systems: A Framed Field Experiment

Saskia Haug, Karlsruhe Institute of Technology (KIT)
Marcel Ruoff, Karlsruhe Institute of Technology (KIT)
Ulrich Gnewuch, Karlsruhe Institute of Technology (KIT)

12:00 AM

The Role of Artificial Intelligence for Business Value

Maggie C. M. Lee, Swinburne University of Technology
Helana Scheepers, Swinburne University of Technology
Ariel K. H. Lui, RMIT University
Eric W. T. Ngai, The Hong Kong Polytechnic University

12:00 AM