Full-Day Workshops Scheduled for 9:00 AM - 4:30 PM:
Half-Day Workshops Scheduled for 8:30 AM- Noon:
Half-Day Workshops Scheduled for 1:30 PM - 5:00 PM:
- Gretchen Macht, University of Rhode Island
- Dr. Michael Byrne, Rice University
- Dr. Philip Kortum, Rice University
Voting is a highly complex ecosystem. Constitutionally, rather than a single set of election rules, election rules exist for each of the 3,000+ counties in the U.S., requiring intentionality when designing systems for election operations. Election administrators are often under-equipped to assess system changes required by law or to increase voter access. As part of their job description, election administrators are required to master 15+ domains with HF/E-related aspects including designing ballots, training poll workers, allocating resources, accommodating all citizens, and planning in-person voting, vote-by-mail, and overseas voting. Election administrators desperately need support, with many requesting help to no avail. Election systems offer a unique opportunity for applied and theoretical HF/E research. Initial research on ballot design, voting errors, and equipment accessibility indicates the start of the work needed in elections. However, many aspects of election systems and voting still need to be explored, and we need your help.
In the spirit of bringing together HF/E professionals with industries where assistance is needed, the HFES Government Relationship Committee presents the Election Integrity workshop, where we call all members of the HFES community, regardless of experience, expertise, or background, to join election experts, government officials, and fellow academics in learning how to get involved in improving election integrity. Several guest speakers, including current and former election officials, government officials, and academic researchers, will present current challenges faced in elections, in light of 2024. Attendees will get the opportunity to learn about elections in the U.S., identify HF/E challenges in elections, brainstorm research avenues, interact with historic voting equipment, and learn firsthand how HF/E currently fits into the election space. Additionally, attendees will be invited to join a network of experts supporting election administrators nationwide.
Join us and prominent election officials to have important conversations about how we at HFES can assist one another in support of election integrity. Funding opportunities for workshop fees and travel are available for HFES community attendees. Potential future seed grant opportunities are available post-workshop. The workshop organization team will provide all materials and equipment.
- Prof. Yili Liu, University of Michigan
As in any mature field of science and engineering, human factors engineering (HFE) needs integrative computational frameworks that unify a large and diverse range of theories and methods. For more than two decades, the workshop instructor and his collaborators have been developing the Queuing Network (QN) Architecture, using it to unify many existing theories and methods, and applying it to a variety of HFE applications.
This workshop describes and illustrates how to use the QN Architecture to evaluate a wide range of theories and methods, and to model and simulate a wide range of human behavior and human-machine systems at various levels of granularity. Step-by-step and alternating between lectures and software demos, this workshop describes the family of QN models that account for phenomena from the micro-neural level to meso-individual level to macro-team or multi-agent level. These "model siblings" all speak the same QN language and share the same QN ideas, but each has its specialties and companions/partners (e.g., AI, ML, Optimization): (1) QN-Reaction Time includes many RT models as special cases; (2) QN-RT-Accuracy bridges QN with Diffusion/Accumulator models to account for Speed-Accuracy Tradeoff (SAT), etc.; (3) QN-MHP integrates QN with Model-Human-Processor (MHP) to model procedure-based multi-task performance; (4) QN-ACT-R integrates QN with ACT-R by implementing and visualizing ACT-R's modules and buffers as QN servers to model multitask performance involving complex cognition; (5) QN-MBS (Mind-Body-System) extends the QN architecture to include body parts; (6) QN-Neural models neurophysiological phenomena; (7) QN-MPMM (Multi-Person Multi-Machine) treats MPMM systems as hierarchically-organized larger QNs.
More siblings are in various stages of incubation (e.g., QN-Control, QN-NSEEV and QN-ACES). QN models were originally coded in commercial simulation software but are now being implemented in Python with easy-to-use interfaces. Model Users do not need to know Python. Developers can delve into and modify the codes. Students/researchers/practitioners of any level of expertise can benefit from this workshop: learn how to use the QN methods/software (get a feel for it), incorporate it in dissertation/theoretical/applied research or product evaluation, and/or extend/challenge the models. A laptop is optional for hearing the lectures and seeing the demos but needed for accessing electronic documents.
- Patricia McDermott, The MITRE Corporation
- Ronna ten Brink, The MITRE Corporation
In this workshop, participants will learn how to design and evaluate autonomy, automation, and AI that partners successfully with humans. Participants will learn about the many facets of human-machine teaming (HMT) as relates to systems with autonomy and practice a set of methods for designing team-based interaction with these emerging systems. Through examples, the instructors will describe the research-based HMT Framework that addresses the need for appropriate observability, cognitive assistance, and coordination. This HMT Framework has ten themes with supplemental design guidelines that can be leveraged to construct a successful partnership between humans and autonomy. Participants will learn methods to develop design guidance that is specific to their system or application. They will practice techniques for gathering human-AI teaming (HAT) information from experts, analyzing interview data, applying findings to design, and conducting evaluations. They will learn when and how to apply these methods. The workshop will be highly interactive with lively discussions and exercises to practice HAT methods in context. Upon completing the workshop, participants will be able to:
1. Understand the wealth of research in HAT and how to use it towards effective design
2. Identify when an opportunity exists to leverage HMT/HAT systems engineering methods
3. Apply HMT/HAT systems engineering methods to their problem domain
Prospective students should be familiar with basic usability concepts and usability research or design methods. This workshop is applicable to researchers, engineers, designers, and project leaders looking to better understand human-AI teaming research, design guidelines, and methods. Participants will gain familiarity with the aspects of HMT/HAT design and evaluation. The morning will be spent dissecting the HMT Framework for design. This involves examples, myths, and general requirements, with lots of facilitated class discussion so participants can learn from each other's experiences and achieve deeper understanding. The afternoon puts that learning into practice. Participants will learn methods for gathering, analyzing, designing, and evaluating. They will then practice using those techniques in the context of an interactive exercise around a use case. Participants should bring their own laptop computers and chargers.
- Edmond Ramly, Indiana University, Bloomington
- Reid Parks, Indiana University, Bloomington
- Prof. Nicole Werner, Indiana University, Bloomington
- Prof. Richard Holden, Indiana University, Bloomington
This workshop introduces fundamental concepts from implementation science and applications to human factors in healthcare. Participants will be able to describe how implementation strategies, determinants, and outcomes can inform the human-centered design of sustainable sociotechnical system interventions in healthcare. Using case studies from human factors research and practice projects from a variety of healthcare settings, we will introduce the three most established implementation science frameworks. Specifically, participants will design and tailor implementation strategies from the expert recommendations for implementing change (ERIC) to address implementation determinants (barriers and facilitators) from the consolidated framework for implementation research (CFIR) and maximize outcomes from the reach, effectiveness, adoption, implementation, and maintenance framework (RE-AIM).
The workshop was developed by the Design and Implementation Sciences Program (Director, Prof. Edmond Ramly) and the Center for Health by Design (Director, Prof. Nicole Werner; Co-Director, Prof. Rich Holden). It leverages our integrative model for change (presented at HFES 2023), our published Implementation Science 101 journal article, and our openly accessible online tool for designing and tailoring implementation strategies. By learning and practicing established frameworks, human factors researchers and practitioners will be able to systematically account for implementation determinants and outcomes in the design of their own healthcare sociotechnical interventions and identify implementation strategies that fit. Target Audience: HF/E Students, Researchers, and Practitioners; Clinicians Welcome!
Note: Please bring a laptop or tablet with charger for an online activity.
- Mark Chignell, University of Toronto
After many false alarms, the age of AI is here. It is no long a question of if AI will change our lives, or when, but rather how. AI is a two-edged sword that might help usher in an age of life extending personalized medicine on the one hand, or autocratic surveillance and loss of individual freedom on the other.
In this workshop participants will learn some of the major issues in AI governance. A model of how human factors can contribute to socially responsible human-AI interaction will also be presented. After laying out the framework of the topic and key issues, the interactive part of the workshop will consist of three case studies that challenge the participants to resolve dilemmas that arise in particular applications of AI. Our goal in each of the case studies will be to peruse an even-handed analysis of both sides of the case, for and against a more comprehensive use of AI in the particular application area. Case study 1 will look at the development of detailed behavioural models of individual people using the Chinese social credit model as an early example of this type of application. Case Study 2 will look at the use of DNA data (e.g., from 23andme) in developing various commercial applications. Case study 3 will look at AI and the law (e.g., the use of AI judges, lawyers, or juries). We will cover as many of the three case studies as time permits. Each case study will have a brief introduction followed by the assigment of a task to be carried out in breakout groups where each group/team argues for, or against, using an expansive AI approach in the case study under consideration. We will then conclude the case study with a synthesis session where we either create a consensus model of how AI should be used in the case, or else we document two contrasting positions that can be taken. After the completion of the case studies we will wrap up the workshop with reflections on lessons learned and suggestions for future research in this area.
Bring your laptop and charger.
Designing Technology for Older Adults with Cognitive Impairments
- Dr. Walter Boot, Weill Cornell Medicine
- Dr. Sara Czaja, Weill Cornell Medicine
- Dr. Wendy Rogers, University of Illinois
- Dr. Neil Charness, Florida State University
As the population ages, we will see a dramatic increase in the number of people experiencing various age-related cognitive impairments ranging from subjective cognitive decline to mild cognitive impairment (MCI) to forms of dementia. For example, MCI, which is a transitional stage between normal aging and dementia, currently impacts 17% of the older adult population. Fortunately, technology solutions offer tremendous potential to support the well-being, quality of life, social connectivity, and independence of the growing population of older adults with a cognitive impairment (CI). Unfortunately, older adults with a CI are often underrepresented in the technology design process. The mission of the Enhancing Neurocognitive Health, Abilities, Networks, & Community Engagement (ENHANCE) Center is aimed at understanding the challenges older adults with a CI confront in everyday and community living; improving interactions with technology among older adults living with a CI due to stroke, traumatic brain injury, or MCI. We are investigating the potential of existing and emerging technologies to support their needs and designing novel technology solutions to support community engagement and independent living.
In this state-of-the-science workshop, participants will learn about the diverse needs and abilities of older adults experiencing CI, the challenges they encounter in everyday living, how technology solutions can support them, and how the design process must be modified to accommodate cognitively diverse groups of older people. Case examples will be presented to demonstrate accommodations and special considerations needed for this population for the entire design cycle, from the needs assessment phase to the usability and efficacy testing phases. An emphasis will be given to the diversity and time varying needs of aging adults with a CI. The session will include interactive design exercises. The session is appropriate for individuals of various backgrounds (industry, academia, government) and levels of knowledge and skill. Lessons learned will be relevant to the successful design and implementation of any technology-based intervention aimed at supporting older adults with CI. Skills and knowledge gained will facilitate the successful realization of the promise of existing and emerging technologies to support the large, growing, and diverse aging population with cognitive impairments.