This workshop intends to scrutinize the shortcomings of current cybersecurity educational models and identify dynamic and forward-thinking approaches to teaching and learning cybersecurity. Best practices in curriculum design and teaching methodologies that helps keep pace with the rapidly evolving nature of cyber threats, bridge the gap between theory and practice, ensure industry relevance, foster interdisciplinary learning, and adapt to new modes of digital and remote education will be discussed.

SWT Leaders:

Dave Croasdell (Primary Contact)
University of Nevada, Reno
davec@unr.edu

James Elste
Colorado Northwestern Community College
james.elste@cncc.edu

This workshop is designed to equip educators with the knowledge and skills necessary to seamlessly incorporate Robotic Process Automation (RPA) into their pedagogical practices. Participants will gain a comprehensive understanding of how RPA can transform the traditional classroom setting, fostering a more interactive and engaging learning environment. Participants are encouraged to bring their laptop and participate in a hands-on instructional section where they will build their own bots using Microsoft Power Automate. This experience will help educators see more clearly how this tool might offer experiential, engaging and accessible ways of teaching fundamental skills.

The workshop will also introduce curriculum resources specifically curated for faculty. These resources encompass a range of materials, from lesson plans to project templates, all aimed at facilitating the integration of RPA into various subjects and courses. The objective is to provide educators with a robust plug and play toolkit that can be adapted and customized to suit their unique teaching needs and objectives. Innovative ideas on how RPA can be used to address contemporary educational challenges and prepare students for a technology-driven future will be discussed.

SWT Leaders:

Bryant Richards (Primary Contact)
Nichols College
Bryant.Richards@nichols.edu

Julia Fearing
Nichols College
Julia.fearing@nichols.edu

John Pollock
Nichols College
John.Pollock@nichols.edu

The Google Cloud Big Data Essentials Workshop is designed for educators and researchers who wish to delve into the Google Cloud Platform (GCP), learn about free programs for any higher education institution, and obtain practical experience creating services through the Google Cloud Console. During the workshop, you will work with various tools ideal for big data analytics and suitable for research or classroom use. These include Dataproc (Spark/Hadoop), BigQuery (a big data warehouse with SQL and off-the-shelf ML), Cloud Storage, Compute Engine, and the GCP LLM capabilities.

This workshop is a great option for individuals interested in teaching cloud computing concepts or for research; no prior cloud computing experience is necessary. Throughout the workshop, you will have access to our lab offerings, allowing you to gain hands-on experience in creating services through the Google Cloud Console. You must bring your laptop to access the workshop content and participate in the hands-on labs.

Upon completing the workshop, you will understand fundamental cloud computing concepts, Google Cloud’s essential services, and some of the most commonly used big data tools.

Agenda:

• Getting set up with GCP
• Hands-on labs
  • Cloud Compute and storage
  • BigQuery for data access and pre-processing
  • BigQuery for ML and LLM capabilities
  • Dataproc for distributed computing with Apache Spark
• Overview of educational resources that Google Cloud offers to educational institutions
• Conclusion

SWT Leader:

Mohammad Soltanieh-ha
Boston University
msoltani@bu.edu

The objective of this workshop is to provide faculty participants with an opportunity to gain ‘hands-on’ experience with healthcare business analytics focusing on curriculum design, as well as healthcare business datasets & problems. This workshop will enable faculty to integrate concepts related to healthcare business analytics directly into curriculum at their university. The workshop is designed to allow participants to learn about an emerging and sought-after skillset while obtaining an exposure to healthcare business data and healthcare business problems. Workshop activities will provide valuable and practical takeaway ideas and solutions for the participants’ own curriculum development experience. Discussion and idea exchange will focus on devising curriculum strategies and building content, incorporating concepts into courses, and navigating the challenges that curriculum integration presents will include sharing resources.  Workshop highlights include –

• Healthcare Business Analytics – What is ‘Healthcare Business Analytics’?
  • Emerging Skillset (Healthcare Business Analytics) Concepts and Use Cases
  • Healthcare Business Analytics –Why it is sought-after?
  • Relevant Use Cases
  • Relevant Data
• Developing a Healthcare Business Analytics Program          
  • Finding academic partners
  • Finding industry partners
  • Collaboration between colleges (Business and Education & Health Professions)
•  “Hands-on” Applications for Curricula
  • Creating a Healthcare Business Analytics concept map
  • Visualizing Healthcare data and Storytelling
  • Wrangling Healthcare data
• Available Resources
  • Resources for Curriculum
  • Healthcare Datasets – BlueCross BlueShield and others
  • Information Systems Department Masters Programs, Enterprise Systems, University of Arkansas

Pre-conference material will be made available for attendees on coordinators’ or other websites.

SWT Leaders:

Timothy Paul Cronan (Primary Contact)
University of Arkansas
pcronan@walton.uark.edu

Kenneth Grifno
University of Arkansas
kgrifno@walton.uark.edu

Susan E. Bristow
University of Arkansas
sbristow@walton.uark.edu

Ron Freeze
University of Arkansas
rfreeze@walton.uark.edu

The NSF Research Traineeship (NRT) program aims to cultivate a diverse workforce in science, technology, engineering, and mathematics (STEM) fields by supporting interdisciplinary, evidence-based traineeships that advance ways for graduate students in research-based master’s and doctoral degree programs to pursue a range of STEM careers.

This panel discussion will explore the impact of the NRT program on fostering interdisciplinary collaboration and preparing graduate students for careers in Data Science and discuss current and future opportunities to participate. Discussion topics include:

• Success stories and challenges faced by NRT-funded Data Science training programs
• Interdisciplinary collaboration and its role in addressing complex Data Science challenges
• The impact of NRT training on graduate student career development and industry partnerships
• Future directions and opportunities for enhancing the NRT program’s effectiveness in advancing Data Science education and research

SWT Leaders:

Dan Port (Primary Contact)
University of Hawaii at Manoa
dport@hawaii.edu

London Thompson
University of Hawaii at Manoa
wt2@hawaii.edu

The objective of this workshop is to provide faculty participants to become familiar with a broad range of technology and data resources for both Business Analytics and Information Technology programs. The workshop will present technology resources (data and systems) from the University of Arkansas Enterprise Systems, housed in the Information System Department, Walton College of Business. Technologies available include: 1) Teradata, 2) IBM Z15, 3) Microsoft SQL Server, 4) SAP S4HANA, 5) SAS VIYA as well as many actual datasets. A review of the large datasets donated by industries will help identify how these technologies have interfaced with both the University of Arkansas and other Universities. NOTE: Access to all resources covered in this workshop is free to the universities requesting use of the systems.

A panel (both faculty participants and industry specialists) will be available to address the support provided by Enterprise Systems in their usage of these technologies. The workshop will include hands-on access to the Enterprise System platform.  Workshop highlights include –

• Enterprise Systems Introduction
  • Technologies available: Teradata, SAP HANA, IBM Z15
  • Exercises & Use Cases: SAS VIYA, SAS EG, SAS EM, SAP, Teradata
  • Datasets: Dillard’s, Sam’s Club, Acxiom, BlueCross BlueShield
  • Platforms: Teradata Studio, HANA Studio, SQL Server Studio
• Integrating Available Technology into the Classroom
  • Starting a Business Data Program – A Case study from Washburn University in Topeka KS
  • Information Systems Majors and Database users
  • Starting a Master of Healthcare Business Analytics at University of Arkansas
• Panel of Enterprise System Support, Data, & Use
  • Pamela Schmidt – Washburn University
  • Yenny Yang – Teradata University
  • Susan Bristow – University of Arkansas
  • Kenneth Grifno – University of Arkansas
  • Ron Freeze, Moderator
•  “Hands on” use in a virtual delivery method
  • Accessing VMWare and the University of Arkansas Enterprise System offerings
  • Connecting to a large dataset
  • Connecting to Teradata and large Data Sets
• Discussion — Resources, Challenges, Curricular Issues, and Research Implications

Preconference material will be made available for attendees on coordinators’ or other websites.

SWT Leaders:

Timothy Paul Cronan (Primary Contact)
University of Arkansas
pcronan@walton.uark.edu

Ronald D. Freeze 
University of Arkansas
rfreeze@walton.uark.edu

Susan E. Bristow
University of Arkansas
sbristow@walton.uark.edu

Kenneth Grifno
University of Arkansas
Kgrifno@walton.uark.edu

This tutorial focuses on teaching participants text mining in the open-source programming languages R and Python. At the end of this tutorial, participants will be able to:

• Discuss the role of text mining in big data analytics
• Understand the main opportunities and challenges in text analysis
• Articulate several conceptual approaches to text analytics
• Explain how to automate the process of collecting textual data
• Describe and use several techniques to analyze large scale textual data
• Describe the role of open-source languages R and Python in text mining

The tutorial will first present a variety of exploratory (inductive) and confirmatory (deductive) text mining techniques. After a brief introduction to the open-source languages and tools used in the tutorial, hands on navigation will be provided through the four text analytics streams, structured into four sessions:

Stream 1 Traditional “Bag-of-Words, Count and Rule-Based Approaches”: is for researchers interested primarily in statistical “bag of words” and count-based approaches to text mining, including exploratory (inductive) keyword and phrase frequency analysis, term-frequency by inverted document frequency (tf*idf), correspondence analysis, and confirmatory (deductive) and rule-based approaches such as the development and application of dictionaries, including sentiment analysis.

Stream 2 Syntactic Parsing and Unsupervised Machine Learning: will present an overview of syntactic parsing and NLP-based approaches to text mining, including Named Entity Recognition (NER); and unsupervised Machine Learning approaches, such as: topic modeling and k-means clustering.

Stream 3 Supervised Machine Learning and Deep Learning: will present an overview of supervised Machine Learning (ML) including: predictive regression and classification models using naïve Bayes, Support Vector Machines (SVM), and random forests; and Deep Learning (DL) approaches including: Dense Neural Networks (DNN), Long Short-term Memory (LSTM) networks, Convolutional Neural Networks (CNNs).

Stream 4 Word Embeddings, Large Language Models, and Transformers: will present an overview of word embeddings, large language models, and transformer models, including Bidirectional Encoder Representations from Transformers (BERT), Generative Pre-Trained Transformers (GPT), and Text-to-Text Transfer Transformers (T5) models. These techniques all underly the generative AI systems like ChatGPT. We will include a discussion of prompt engineering for generative AI by demonstrating how to use GPT 3.5 Turbo.

All tutorial participants will be asked to prepare for the tutorial in advance by downloading Anaconda Navigator, as their Python environment and package manager, and R and RStudio, for their Integrated Development Environment (IDE) for both R and Python. Anaconda Navigator will also provide access to Jupyter Notebooks for use as an IDE. We will make the data for the tutorial available for all participants in the room. In addition to the HICSS paper corpus, we will include textual datasets focused on cybersecurity and AI (e.g. RFI comments on NIST AI RMF, CERT incident reports, and CVEs). After registering for the tutorial, each participant will receive access to the private GitHub repository for the tutorial. The repo will contain the datasets, and other materials for use during the tutorial.

SWT Leaders:

Derrick Cogburn (Primary Contact)
American University
dcogburn@american.edu

Haiman Wong
Purdue University – West Lafayette
wong424@purdue.edu

Theodore A. Ochieng
American University
to9648a@american.edu

This workshop aims to spotlight the prowess of free and open-source modeling environments such as the KNIME Analytics Platform as ideal tools for intuitive teaching, learning, and practical application development across business analytics, data science, and machine learning realms. It will showcase the best practices in business analytics and data science, underscoring the seamless modeling capabilities of these software tools. Participants will have the opportunity to engage hands-on by installing the platform directly onto their laptops during the workshop, enabling them to follow along with the demonstrations effortlessly.

The workshop will also delve into the transformative potential of generative AI in harnessing raw text data to furnish knowledgeable answers. It will guide participants through the process of leveraging generative AI to craft a custom chatbot without the need for coding, utilizing the KNIME Analytics Platform.

Preliminary Agenda

0-10 minutes: Welcome and introductions
20-30 minutes: Introduction to Low Code Data Science with KNIME Analytics Platform
0-10 minutes: Q&A Session
60 minutes: Hands-On Session: Build your first workflow with KNIME
20-30 minutes: Introduction of GenAI and Codeless Chatbot Development with KNIME
0-10 minutes: Q&A Session
60-120 minutes: Hands-On Session: Build your Custom Chatbot Data App Building
10-15 minutes: Demonstrating the exercise solutions

SWT Leaders:

Dursun Delen (Primary Contact)
Oklahoma State University, Stillwater
dursun.delen@okstate.edu

Rosaria Silipo
KNIME AG.
rosaria.silipo@knime.com

This workshop is designed for educators who are interested in teaching descriptive analytics and data visualization, using large datasets from the United States Government, and a new open-source data language, Malloy.

During the workshop, you will be introduced to various practical tools for big data analytics for research or classroom use. These include Parquet files (a free and open-source column-oriented data storage format from the Apache Foundation), VSCode (a free and open-source code editor, and most popular programming tool since 2018), DuckDB (an open-source column-oriented relational database management system focused on analytics), Malloy (an open-source language for analyzing, transforming, and modeling data made by engineers at Meta and Google), and Google’s BigQuery (a serverless data wharehouse with very-large datasets available to students).

This workshop is for individuals interested in teaching analytics in engaging ways by allowing students to bring their own data for analysis. We will discuss several freely available datasets that the author has used with students, including government data from the securities and exchange commission, the data liberation project and the National Highway Traffic Safety Administration.

Many programs are using PowerBI, Tableau, and Alteryx all of which are costly and closed-source, in-contrast, we demonstrate Malloy as a powerful replacement tool. Participants will learn the basics of the Malloy language, and demo advanced features of the language.

Malloy is an open-source data query language that is easier to read, write, and comprehend and compiles to SQL. It allows for semantic data modelling which enables comprehensibility, and preservation of business logic. For example, you can query a local database, a local CSV file, and a cloud database at the same time. Daunting SQL queries are straightforward in Malloy. The nesting feature allows for rich hierarchical views of data.

Upon completing the workshop, you will understand fundamental open-source data tools for teaching analytics of very large datasets on both local and cloud-hosted data.

Agenda

• Getting set up with VSCode and Malloy
• Hands-on labs
  • Industry Trends from Public Companies (based on Financial Statement Data)
  • Trends from Moving Headquarters of Public Companies
  • Profitability of all Public Companies
  • Traffic Deaths in the United States
  • Prison Complaints in the United States
• Overview of educational resources from Google Cloud, and Malloy
• Overview of freely available very large datasets
• Discussion of how Malloy fits into analytics curriculum
• Conclusion

SWT Leader:

Tim Olsen
Gonzaga University
olsent@gonzaga.edu

[Agenda]

Modern social science is facing a paradigm shift due to the development of computer and Internet technologies. Human behavior and social phenomena are possible to be quantified by big data digitally tracing online activities and mobility records at a granular level. In some cases, big data can be analyzed using technologies evolving in the natural sciences, such as physics, chemistry, and biology. Experimental data and multiple results from theoretical and computational simulations complement them. Both theoretically and analytically grounded insights may open new doors of computational social sciences.

Based on the above perspective, this symposium will present issues related to computational social science such as big data applications, big data collection and use, an integrated framework for theory, simulation, statistics, and experiments.

SWT Leaders:

Isamu Okada (Primary Contact)
Soka University
okada@soka.ac.jp

Fujio Toriumi
University of Tokyo
tori@sys.t.u-tokyo.ac.jp

Dongwoo Lim
Tsuda University
dongwoo.lim@tsuda.ac.jp

[Agenda]

In this tutorial, participants will learn state–of–the–art data and software management practices to support preservation and reproducibility. Fundamental component of FAIR principles will be introduced to familiarize participants with good management practices required to ensure that data, software, and knowledge are properly preserved throughout the life cycle of a project and beyond.

The tutorial will be organized around three mini-sessions, each with a short demo or presentation of tools and the bulk of time devoted to hands-on participatory activities. Tools will be grouped around repositories, i.e., the DMPTool for planning how data and software are managed in the lab and the Open Science Framework as an example of a collaboration platform facilitating data management and sharing. The tutorial will conclude with a panel of open questions, including suggestions for future areas of emphasis for this tutorial.

SWT Leaders:

Line Pouchard (Primary Contact)
Sandia National Laboratory
lcpouch@sandia.gov

Sandra Gesing
San Diego Supercomputer Center
sgesing@ucsd.edu

Natalie Meyers
University of Notre Dame
natalie.meyers@nd.edu

This symposium convenes a diverse group of research operations experts and clinician-scientists to discuss the range of obstacles that that must be overcome before clinical research can be performed on real patients in medical centers. As tech-heavy research expands in patient care settings, attendees will benefit from understanding the protocols, reviews, and approvals needed from the medical centers before research may begin.

Topics of discussion include, but are not limited to, protected health information, approaches to data use and data sharing agreements, billing issues, indemnification, requirements for device cleaning and sterilization (that extend even for smartphone app trials), and information security. Real-world examples from research and clinical trials, highlighting both successful outcomes and lessons learned from less successful endeavors will be presented.

SWT Leaders:

Edward W Boyer (Primary Contact)
Ohio State University Medical School and Harvard Medical School
Edward.boyer@osumc.edu

Guruprasad Jambaulikar
Brigham and Women’s Hospital
gjambaulikar@bwh.harvard.edu

Jennifer Frey
Ohio State University College of Medicine
jennifer.frey2@osumc.edu

Over the past few years, interests in artificial intelligence (AI), large language models (LLM), and other digital solutions have exploded in health care. The advancement not only presents new challenges and opportunities but also has changed the expectations and operations of health systems. These innovations, such as AI-guided disease screening and decision support, hospital-at-home programs, and LLM to assist with clinical note-writing, are often developed by embedded scientists. Unlike drugs, devices, or other traditional interventions that are presented to the health system as fully tested user applications, interventions in the current digital age by nature evolve within the health system, and the evaluation and implementation require a much shorter timeline and additional considerations from global public health, technical, ethical, and legal perspectives. This symposium will provide diverse perspectives from data science, health care delivery research, human-computer interaction, and health disparities to offer state-of-art deep thoughts on AI evaluation and implementation to drive positive systemic change in healthcare.

Tentative Agenda

9:00 – 9:05
Welcome and Introduction | Xiaoxi Yao

9:05 – 9:15
How Mayo Clinic Met Unprecedented Needs with Unprecedented Solutions in Digital Age | Elizabeth Habermann

9:15 – 9:30
Development and Deployment of Deep Learning and Large Language Models in Routine Clinical Care | Zachi I. Attia

9:30 – 9:45
Meeting Patient and Clinician Needs: The Role of Transparency and Trust in the Adoption of Healthcare AI | Barbara Barry

9:45 – 10:00
Health Impact Evaluation of AI in Real World | Xiaoxi Yao

10:00 – 10:15
Q&A

10:45 – 11:05
Demystifying Scientific Development of AI and the Role of Leading Medical Journals | Rohan Khera

11:05 – 11:25
Disparity in the Digital Age and Learning Organizations | Joshua C. Rubin

11:25 -11:45
How Synchronized Global Standards Can Facilitate Trustworthy AI-enabled Research, Rebecca D. Kush

11:45 – 12:00
Q&A and Panel Discussion

SWT Leaders:

Xiaoxi Yao (Primary contact)
Mayo Clinic
yao.xiaoxi@mayo.edu

Rohan Khera
Yale School of Medicine
rohan.khera@yale.edu

Joshua C. Rubin
University of Michigan Medical School
josh@joshcrubin.com

The use of wireless devices to collect various types of critical mobility and medical data continues to grow – both in the commercial world, as well as in the research domain. The impact of such devices remains limited though, primarily due to the lack of sophisticated data analytics tools to allow for the extraction of useful information out of the collected data. This tutorial will address these issues with a particular focus on the following objectives:

• Provide an overview of available wearable devices and the types of mobility and medical data such devices can collect, as well as discuss current research studies associated with the use of mobility and medical data in biomedical research and healthcare.
• Introduce the main ideas associated with obtaining mobility patterns or signatures using raw data collected from wearable devices and use AI filtering tools to identify the key mobility/medical parameters needed to assess the health level of individuals.
• Introduce the basic concepts related to using correlation and similarity networks to store, visualize and analyze data associated with different applications in the biomedical informatics domain and show the potential of using these networks as a key component of an advanced decision support system for next-generation healthcare.
• Introduce the participants to how graph models and complex networks can be developed using mobility and medical data to assess health levels for various groups. The main goal of the proposed models is to classify health levels, track their health variability pattern, and predict potential health hazards.
• Show how the proposed approach can be used specifically for the early diagnosis of health issues associated with early childhood development, developmental disorders, and aging conditions.

SWT Leader:

Hesham H. Ali
University of Nebraska at Omaha
hali@unomaha.edu

Wearable devices and body sensor technology have been increasingly used for health monitoring to capture continuous human body data in unprecedented quantity and quality. While various existing devices collect different types of data, the accessibility of these data is however limited to manufacturers’ internal team of developers which in turn hinders the advancement in the development of health condition prediction and prevention.

This workshop is designed to promote the building of an open-source community – Boracle –  that provides suggestions for the selection of smart wearable devices for data collection as well as supports data integration and intelligent analysis. In addition to exploring the use of smart wearable devices for health monitoring and the use of AI for health prognostic, this workshop will also discuss issues related to security, privacy, regulations, and interoperability challenges faced in the development of the open-source community and mobile applications.

SWT Leaders:

Nhut Ho (Primary Contact)
California State University, Northridge
nhut.ho.51@csun.edu

Trung Dung
anphu06@gmail.com

Xunfei Jiang
California State University, Northridge
xunfei.jiang@csun.edu

This symposium intends to explore how augmented intelligence and augmented reality can improve the future of work, with benefits for workers, employees, and/or society. It will examine the risks and identify paths toward understanding and eventually minimizing potential harms caused by any disruptive technology while preserving key benefits. The following questions will be addressed.

• In what ways can organizations foster effective collaboration between humans and generative AI systems, and how might this collaboration reshape traditional workflows?
• What principles should guide the development of human-AI interaction models to enhance productivity and innovation?
• How will generative AI and AR influence the nature of jobs in the future, and what new types of roles or industries might emerge as a result?
• What challenges and opportunities does the evolving job landscape pose for both employers and employees?
• How do generative AI and AR contribute to or challenge human creativity, and what opportunities does it present for innovation in various industries? Can generative AI be a catalyst for more creative and dynamic work environments?
• How will generative AI influence global workforce dynamics, including outsourcing trends, remote work, and cross-border collaborations? What challenges and opportunities arise in managing a globally distributed workforce in the context of generative AI?
• Are there regulatory frameworks or policies that should be established to govern the ethical and responsible deployment of generative AI in the workplace?
• What are key ethical risks and legal issues that threaten to reduce the benefits of these emerging technologies of human augmentation for workers, organizations, and society?
• How should ownership and intellectual property rights be defined and protected in the context of generative AI systems trained on proprietary datasets?
• What mechanisms can ensure fair compensation for owners of data used to train AI models, especially when the resulting models generate significant value?
• What considerations should organizations consider when negotiating data licensing and usage agreements with data providers, particularly in scenarios where generative AI is involved?

SWT Leaders:

Souren Paul (Primary Contact)
Northern Kentucky University
Souren.paul@gmail.com

Tung Bui
University of Hawaii at Manoa
tungb@hawaii.edu

Alex Kass
Accenture Labs
alex.kass@accenture.com

Across a wide range of industries, digital platforms have become the dominant business model and many of the world’s most valuable companies are platform-based. Digital platform businesses, such as Airbnb, Uber, Alibaba and Tencent, have disrupted conventional industries by introducing innovative business models to the market. Increasingly, companies in traditional sectors like airlines, automotive, banking and pharmaceuticals, embrace digital platforms to reshape their opera8ons and industries. The ascendance of digital platforms alters the landscape of strategy and competition, because digital platforms have distinct attributes such as network effects and winner-takes-all market dynamics.

This workshop provides an introduction to system dynamics simulation modeling of digital platforms. In this workshop, participants will build a strategic model to explore the competitive dynamics of digital platforms and the trade-off between investing in high platform performance versus reducing investment in order to facilitate third party content development. The workshop learning outcomes for participants include: (a) developing simulation modeling skills and (b) synthesizing knowledge about digital platform competitive dynamics that could be applied later to research or teaching.

SWT Leaders:

Shayne Gary (Primary Contact)
University of New South Wales
sgary@unsw.edu.au

Edward Anderson
University of Texas
edanderson@utexas.edu

Mark Heffernan
Dynamic Opera8ons
mark@dynamicopera8ons.com.au

HICSS is a unique conference in its ability to bring together a diverse group of social scientists, engineers, and clinicians from across the globe. Planning HICSS with a special focus on diversity, equity, accessibility, and inclusion (DEAI) ensures that current and next generation of leaders understand the importance of DEAI within the HICSS community, its academic work and scholarship and the HICSS spirit of collaboration.

This workshop will focus on three recommendations derived from HICSS-57 held in 2024 namely:

• Creation of a DEAI Statement for the HICSS conference
• Development of mentorship program(s) to assist authors who may have important science to share but may struggle with generating a high-quality English language manuscript that meets the HICSS publication standards
• Development of toolkits to help mintrack chairs increase diversity through advertising, recruiting and review processes

SWT Leaders:

Stephanie Carreiro (Primary Contact)
University of Massachusetts Chan Medical School
Stephanie.Carreiro@umassmemorial.org

Peter Chai
Harvard Medical School
PCHAI@bwh.harvard.edu

Rochelle Rosen
Brown University School of Public Health
rochelle_rosen@brown.edu

The rise of “new economic statecraft”—the intervention in trade and investment for foreign policy reasons—is increasingly threatening the stability of the global economic system. We have observed countries using various measures to influence trade and investment in high technology, particularly.

This panel will explore leading dual-use technologies, including AI, quantum computing, cybersecurity, and synthetic biology, from technological, political, business, and economic perspectives. Key questions to be addressed include: How are these technologies evolving? What actions are governments taking in these sectors to promote or impede global competition? How are firms in different countries coping with the increasing tension between the U.S. and China? What are the implications for the institutional management of new economic statecraft in these technologies through bilateral, minilateral, and multilateral arrangements? How does or should the “new economic statecraft” influence the information technology strategy of firms?

Panelists (as of August 2024):

• Vinod Aggarwal, UC Berkeley APEC Study Center, Alann P. Bedford Endowed Chair of Asian Studies in the Travers Department of Political Science, and Affiliated Professor in the Haas School of Business
• Tobias Peyerl, Lead, Strategic Intelligence, Open AI
• Third  and fourth Panelists (TBC)
• Tung Bui (moderator), Matson Navigation Chair of Global Business, Professor of Information Technology Management at Shidler College of Business, Director of UH APEC Study Center

SWT Leaders:

Vinod K. Aggarwal (Primary Contact)
UC Berkeley
vinod@berkeley.edu

Tung Bui
University of Hawaii at Manoa
tungb@hawaii.edu

Tobias Peyerl
Open AI

Helmut Krcmar
Technical University of Munich
helmut.krcmar@tum.de

The rapid evolution of artificial intelligence (AI) technologies is catalyzing profound transformations across industrial sectors, reshaping social dynamics, and offering innovative approaches to environmental sustainability. AI systems provide a spectrum of capabilities, from boosting productivity and efficiency to addressing some of the world’s most complex challenges. Yet, as AI capabilities evolve into more advanced, autonomous, and integrated systems, the challenges of ensuring these innovations are designed and governed responsibly grow correspondingly. AI designers must proactively integrate ethical considerations into the foundation of these technologies to ensure they are both innovative and aligned with societal values and norms. Similarly, policymakers need to be aware of the diverse challenges and opportunities that come with AI deployment across organizations and jurisdictions.

This agenda-setting workshop will feature an opening talk by Dr. Dorothy Leidner, Leslie H. Goldberg Jefferson Scholars Foundation Distinguished Professor in Business Ethics, followed by insights from a group of expert panelists and an interactive breakout session. This workshop will cultivate strategies that ensure AI innovations not only drive progress but also adhere to the highest standards of responsibility. Discussions will explore how to balance ethical considerations with the demand for technological advancements in designing next-generation AI and how governance frameworks can ensure accountability, transparency, and ethical oversight throughout the AI lifecycle. Participants will have the opportunity to collaborate on these critical issues, explore future research opportunities, and discuss potential topics for special issues.

Discussion questions include (but are not limited to):

1. What new ethical challenges arise with the advancement of next-generation AI, and how can they be addressed during the design and governance phases?
2. What tradeoffs arise between the demand for AI advancements and existing ethical frameworks, and how can they be navigated responsibly?
3. What are the key challenges and opportunities in implementing distributed governance frameworks for next-generation AI?
4. How can diverse stakeholder groups collaboratively design governance mechanisms that ensure transparency, accountability, and inclusivity in AI development?
5. How can distributed governance models guide the responsible development and deployment of AI across different sectors and jurisdictions?

SWT Leaders:

Jenifer Sunrise Winter (Primary Contact)
University of Hawaii at Manoa
jwinter@hawaii.edu

Kaveh Abhari
San Diego State University
kabhari@sdsu.edu

Andrea Rivera
University of Hawaii at Manoa
alrivera@hawaii.edu

Bo Sophia Xiao
University of Hawaii at Manoa
boxiao@hawaii.edu

Influence operations refer to campaigns that either spread misinformation (i.e., false, or misleading information) or try to manipulate how a group of people see the world. Disinformation campaigns have a long history (e.g., influencer marketing campaigns, the use of Propaganda in historic wars).

The use of technology such as social media platforms, artificial intelligence (AI), and data analytics, have meant that they can be done at scale, and impact a much wider community at a greater speed. The increase in the use of AI technologies to generate content for manipulative information campaigns in politics has been well documented. Major technology firms have pledged their effort in fighting disinformation campaigns related to global elections. However, industry, academia and government must work together if the role of technology in detecting, facilitating, and combatting influence operations is to be fully understood.

This workshop will bring together representatives from the different stakeholders to develop an interdisciplinary research framework that is focused on understanding the role of technology in influence operations. Particular attention will be made to the legal/ethical considerations of privacy, freedom of speech and regulation. It will build on the recent discussions and findings of the RAAINS workshop (Recent Advances in Artificial Intelligence for National Security) and various influence operation conferences (e.g., Cambridge Disinformation Summit).

SWT Leaders:

Zena Wood (Primary Contact)
University of Exeter
z.m.wood2@exeter.ac.uk

Raul Harnasch
Lincoln Laboratory MIT
raul.harnasch@ll.mit.edu

Dennis Ross
Lincoln Laboratory MIT
dennis.ross@ll.mit.edu

This workshop aims to create a space to collaboratively and creatively discuss (1) online abuse of researchers, (2) it’s impacts on scholarly endeavors, and (3) explore possible interventions to mitigate its harms. The nature of online harassment and its possible impacts on academics, drawing both from the literature and the experiences of academics who have dealt with online abuse, will be examined. A list of possible solutions and recommendations outlining what academic institutions, organizations such as HICSS, and academics can do to address harassment on digital platforms will collaboratively be created.

SWT Leaders:

Esteban Morales (Primary Contact)
Royal Roads University
esteban.morales@royalroads.ca

Jaigris Hodson 
Royal Roads University
jaigris.hodson@royalroads.ca

Victoria O’Meara
University of Leicester
vjom1@leicester.ac.uk

Anthropogenic climate change presents a pressing economic challenge, with global warming already exceeding one degree Celsius since pre-industrial times. The circular economy (CE) has emerged as a response, aiming to reduce resource demand and waste generation through strategies like reuse, refurbishing, and recycling. Circular approaches are – especially in terms of reuse or refurbishing/upcycling – heavily dependent on the availability of easy-to use IT solutions as these solutions depict a necessary precondition for an effective organization of an exchange between peers that do not share a personal network, but only an interest in fostering a more sustainably lifestyle. By now, CE approaches are considerably diverse in terms of employed IT solutions which – combined with the fact that CE lacks a comprehensive environmental philosophy – is restricting the ability of such approaches to address sustainability trade-offs effectively.

Furthermore, cultural factors that play a significant role in CE implementation are often overlooked. CE approaches are mainly viewed as uniform, general solutions to worldwide, environmentally triggered economic challenges. However, the degree of concern about anthropogenic climate change and its consequences as well as the acceptance of circular – often more difficult to handle and more expensive – approaches seems to vary greatly globally.

This workshop aims to explore the cultural dimensions of CE adoption and to link it the acceptance of circular strategies. By bridging the gap between theory and practice, the workshop seeks to develop a blueprint for culturally informed CE research and implementation, essential for addressing the global challenge of climate change.

SWT Leaders:

Sven M. Laudien (Primary Contact)
media Akademie – Hochschule Stuttgart
laudien@media-hs.de

Jantje Halberstadt
University of Vechta
Jantje.Halberstadt@uni-vechta.de

Sustainable computing is an emerging crucial area of national priority. A major societal goal for sustainable computing is to make the massive computing infrastructures (such as large-scale data centers) in all private and public sectors economical and environmentally friendly. The format of this symposium will be that of a “town hall meeting” with plenty of opportunities for questions, answers, and discussions. Topics of discussion include (but are not limited to) power-aware algorithm and software design; power-efficient hardware and system design; power-awareness characterization, metrics, and modeling; thermal behavior and control; life cycle analysis of computing infrastructures; pervasive sustainability; and smart grid and energy supply-and-demand matching.

SWT Leaders:

Adolfy Hoisie (Primary Contact)
Brookhaven National Laboratory and Stony Brook University
ahoisie@bnl.gov

Behrooz Shirazi
Washington State University
shirazi@wsu.edu