SIG AIAA at AMCIS 2024
Track on Artificial Intelligence and Autonomous Applications
Track Chairs
Track Description
The Artificial Intelligence and Autonomous Applications track features research on a wide range of topics related to artificial intelligence (AI) and machine learning (ML) algorithms, including novel applications of algorithms (e.g., recommendation systems, information security analytics, healthcare, etc.), emerging types of AI agents (e.g., conversational agents, digital humans, etc.), and socio-economic aspects of AI and algorithms (e.g., ethical AI, biases in data and algorithms, impact on jobs, regulations, etc.). The track welcomes all research methods, including design science, behavioral, and economics.
Minitracks:
AI, Emotions, and Empathy
AI-enabled technologies have been permeating human lives and societies at a growing rate over the last three decades. They started at the mechanical task levels (e.g., manufacturing robots) and slowly made their way into analytical tasks (e.g., personal assistants, traders, schedulers, etc.). However, these technologies are still finding their way into the realm of human emotions and empathy. Hence, emotional and empathic AI is expected to be the next frontier of AI research and development. Likewise, the role of emotions in human-technology interaction is a growing research area within the IS discipline. Accordingly, this forward-looking mini-track welcomes all kinds of theoretical and empirical research at the intersection of human emotions, empathy, and AI.
Reza Vaezi, Kennesaw State University, svaezi@kennesaw.edu
Maryam Ghasemaghaei, McMaster University, ghasemm@mcmaster.ca
Mohsen Jozani, Augusta University, mjozani@augusta.edu
AI in Higher Education Information Systems
AI, especially Generative AI (GAI), is transforming information systems in higher education, revolutionizing the way students learn, faculty teach, and researchers conduct research. This minitrack explores how AI and GAI tools, such as AI-driven chatbots for academic advising, embedded AI in Learning Management Systems, and AI-empowered research capabilities, are being deployed to improve student success, automate administrative workflows, and enhance research productivity. We also welcome papers that explore the ethical, socioeconomic, and workforce implications of AI in higher education, including data privacy and algorithmic fairness, the future of academic employment, and the impact of generative AI on student success and learning outcomes.
We encourage submissions that employ various research methods, from design science to behavioral and economic analyses, to provide a comprehensive understanding of how AI and GAI are shaping the future of higher education information systems.”
Yun Wan, University of Houston – Victoria wany@uhv.edu
Xiwei Wang, Northeastern Illinois University, xwang9@neiu.edu
Generative AI and Conversational Agents: Shaping the Future of Work
Conversational AI agents like chatbots and voice assistants are becoming integral components of teams, organizations, and daily operations. Historically, humans have employed these agents to handle basic and repetitive tasks, streamlining processes and improving efficiency. These systems evolve rapidly, especially due to generative AI advances, and are taking on more complex and strategically relevant roles within organizations. This raises essential questions about the nature of human-AI collaboration. We invite submissions that draw upon design science, empirical studies, action research, or case studies to explore the dynamics of human-AI interactions, collaboration, and the behavioral nuances in contexts where joint value creation with AI agents is paramount.
Dominik Siemon, LUT University, dominik.siemon@lut.fi
Edona Elshan, Vrije University Amsterdam, e.elshan@vu.nl
Bijan Khosrawi-Rad, Technische Universität Braunschweig, b.khosrawi-rad@tu-braunschweig.de
Promises and Perils in Ethics and Management of Artificial Intelligence: Disruption, Adoption, Dehumanisation, Governance, Risk and Compliance
In the last decade, Artificial Intelligence (AI) has transitioned from a peripheral technology to a dominant driver of innovation. It is now routinely used to recognize images, parse speech, respond to questions, make decisions, and even replace humans.
Generative AI presents exciting possibilities, from text generation to image synthesis, but it also brings ethical challenges. Misused generative AI tools can breach privacy, jeopardize safety, and make unethical decisions. To navigate this landscape effectively, researchers and practitioners must understand the state of the art, adoption, and influence of AI and ML, while also addressing the ethical and governance mechanisms needed to safeguard human well-being.
This mini track focuses on the ethics and management of AI, with a particular emphasis on adoption, disruption, dehumanization, and governance, risk, compliance, and the ethical mechanisms required to protect and enhance human well-being. We welcome a wide range of papers with qualitative and quantitative orientations, offering both theoretical and practical contributions, from personal, organizational, and societal perspectives.
Valeria Sadovykh, University of Auckland, valeriasadovykh@gmail.com
David Sundaram, University of Auckland, d.sundaram@auckland.ac.nz
Kevin Craig, Auburn University, kac0117@auburn.edu