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GVU Center

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https://hdl.handle.net/1853/70651

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Interactive Media Technology Center

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https://finding-aids.library.gatech.edu/agents/corporate_entities/1093

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Publication Search Results

Now showing 1 - 10 of 629

Critical AI literacy with children: in pursuit of fair and inclusive technology futures

(Georgia Institute of Technology, 2024-03-14) Sharma, Sumita

Children interact with Artificial intelligence (AI) in various direct and indirect ways, yet, there is limited research on the impacts of AI on children. Further, these studies mainly focus on cultivating, nurturing, and nudging children towards technology use and design, without promoting critical perspectives towards AI. For intance, there is little discussion with children on the limitations, inherent biases, and lack of diversity in current design and development of AI, and on critical examination of the ethical aspects of technology use, design, inherent limitations, and consequences of these on children and society at large. In this talk, I will present my work on critical AI literacy with young children, sharing lessons from hands-on workshops with children in Finland, India, and Japan.
Foley Scholar Winner and Finalists Presentations Spring 2024

(Georgia Institute of Technology, 2024-03-07) Bhat, Karthik Seetharama ; Narechania, Arpit ; Pendse, Sachin ; Riggs, Alexandra Teixeira

Foley Scholar Award Winner: Envisioning Technology-Mediated Futures of Care Work, Karthik Seetharama Bhat. Caregiving is a universal activity that is receiving increasing attention among technologists and researchers in the wake of the COVID-19 pandemic. Emerging technologies like conversational AI, augmented and virtual reality, and smart homes have all been described as potentially revolutionary technologies in care work, intended to automate and transform the overall care experience for caregivers and care recipients. However, such promises have yet to translate to successful deployments as these technological innovations come up against socioculturally situated traditions of care work that prioritize human connection and interaction. In this talk, I will share empirical studies looking into how formal care workers (in clinical settings) and informal care workers (in home settings) reconcile technology utilization in care work with sociocultural expectations and norms that dissuade them. I will then discuss possible technology-mediated futures of care work by positing how emerging technologies could best be designed for and integrated into activities of care in ways that unburden care workers while ensuring quality care.
Exploring Dual Perspectives in Computer-mediated Empathy

(Georgia Institute of Technology, 2024-02-29) Lee, Sang Won

A common belief is that technology can play a pivotal role in enhancing individuals' capacity to empathize with others. While it is true, it's worthwhile to adopt an alternative perspective that underscores the inherent duality of empathy and emphasizes the empowering aspect for the recipients of empathy. In this talk, I will focus on recent projects that explore how technologies can facilitate empathy. These approaches primarily focus on those who need to be empathized and help them express, reveal, and reflect on themselves. Through these works, I propose a new framework that offers various research topics relevant to enhancing computer-mediated empathy.
An Introduction to Healthcare AI

(Georgia Institute of Technology, 2024-02-22) Braunstein, Mark

Healthcare and AI have an intertwined history dating back at least to the 1960's when the first 'cognitive chatbot' acting as a psychotherapist was introduced at MIT. Today, of course, there is enormous interest in and excitement about the potential roles of the latest AI technologies in patient care. There is a parallel concern about the risks. Will human physicians be replaced by intelligent agents? How might such agents benefit patient care short of that? What role will they play for patients. We'll explore this in a far-ranging talk that includes a number of real-world examples of how AI technologies are already being deployed to hopefully benefit those physicians and their patients.
Whose Responsibility? The Case for Responsible Data Practice

(Georgia Institute of Technology, 2024-02-15) Wang, Ding

Diversity in datasets is a key component to building responsible AI/ML. Despite this recognition, we know little about the diversity among the annotators involved in data production. Additionally, despite being an indispensable part of AI, data annotation work is often cast as simple, standardized and even low-skilled work. In this talk, I present a series of studies that aim at unpacking the data annotation process with an emphasis on the data worker who lifts the weight of data production. This includes interview studies to uncover both the data annotator’s perspective of their work and the data requestor’s approach to the diversity and subjectivity the workers bring; an ethnographic investigation in data centers to study the work practices around data annotation; a mixed methods study to explore the impact of worker demographic diversity on the data they annotate. While practitioners described nuanced understandings of annotator diversity, they rarely designed dataset production to account for diversity in the annotation process. This calls for more attention to a pervasive logic of representationalist thinking and counting that is intricately woven into the day to day work practices of annotation. In examining structure in which the annotation is done and the diversity is seen, this talk aims to recover annotation and diversity from its reductive framing and seek alternative approaches to knowing and doing annotation.
Trans Technologies

(Georgia Institute of Technology, 2024-01-25) Haimson, Oliver

Haimson's forthcoming book Trans Technologies (MIT Press, 2025) examines the world of trans technologies: apps, health resources, games, art, AR/VR, and other types of technology designed to help address some of the challenges transgender people face in the world. His research team conducted in-depth interviews with more than 100 creators of existing trans technologies to understand the current landscape, highlight areas for future innovation, and build theory via community input around what it means for a technology to be a trans technology. This work illuminates the people who create trans technologies, the design processes that brought these technologies to life, and the ways trans people often rely on community and their own technological skills to meet their most basic needs and challenges. He discusses how trans technology design processes are often deeply personal, and focus on the technology creator’s own needs and desires. Thus, trans technology design can be empowering because technology creators have agency to create tools they need to navigate the world. However, in some cases when trans communities are not involved in design processes, this can lead to overly individualistic design that speaks primarily to more privileged trans people’s needs. Further, he discusses some of his research group’s ongoing participatory design work designing trans technologies.
Democratizing Robot Learning and Teaming

(Georgia Institute of Technology, 2023-09-14) Gombolay, Matthew

New advances in robotics and autonomy offer a promise of revitalizing final assembly manufacturing, assisting in personalized at-home healthcare, and even scaling the power of earth-bound scientists for robotic space exploration. Yet, in real-world applications, autonomy is often run in the O-F-F mode because researchers fail to understand the human in human-in-the-loop systems. In this talk, I will share exciting research we are conducting at the nexus of human factors engineering and cognitive robotics to inform the design of human-robot interaction. In my talk, I will focus on our recent work on 1) enabling machines to learn skills from and model heterogeneous, suboptimal human decision-makers, 2) “white-box” that knowledge through explainable Artificial Intelligence (XAI) techniques, and 3) scale to coordinated control of stochastic human-robot teams. The goal of this research is to inform the design of autonomous teammates so that users want to turn – and benefit from turning – to the O-N mode.
Shape Machine: From software to practice

(Georgia Institute of Technology, 2023-09-07) Economou, Athanassios

What would it mean if we could select any part (shape) of a CAD model and use it to find (⌘F) all its geometrical instances in the model (or other CAD models for that matter) – same size, larger, smaller, rotated, reflected or transformed in some way? What would it mean if we could edit this part and use it to replace (⌘R) all its geometrical instances in the model? Why is that the Find and Replace (⌘F/⌘R) operations that are so essential in Word or Excel have yet to be implemented in CAD? And what would happen if we could seamlessly use these shape-based Find and Replace (⌘F/⌘R) operations in a logical processing framework using states, loops, jumps and conditionals to literally write programming code by drawing shapes? How would this affect our current view of computation and what would it mean for design? The talk discusses the current state of the Shape Machine, a shape-rewrite computational system that features shape-based Find and Replace (⌘F/⌘R) operations for lines and arcs in 2D vector graphics and a logical processing framework including familiar control flow constructs (looping and branching), to allow write programming code by drawing shapes. Shape Machine is developed at the Shape Computation Lab at the Georgia Institute of Technology and currently is integrated within Rhinoceros, a NURBS 2D/3D CAD software. Several applications drawn from architectural design, industrial design, game design, circuit design, mathematics and other fields showcase the potential impact of this new technology in various domains.
Considering People and Technology

(Georgia Institute of Technology, 2023-08-24) Best, Michael L.
Computational Models of Human-Like Skill and Concept Formation

(Georgia Institute of Technology, 2023-04-13) MacLellan, Christopher J.

The AI community has made significant strides in developing artificial systems with human-level proficiency across various tasks. However, the learning processes in most systems differ vastly from human learning, often being substantially less efficient and flexible. For instance, training large language models demands massive amounts of data and power, and updating them with new information remains challenging. In contrast, humans employ highly efficient incremental learning processes to continually update their knowledge, enabling them to acquire new knowledge with minimal examples and without overwriting prior learning. In this talk, I will discuss some of the key learning capabilities humans exhibit and present three research vignettes from my lab that explore the development of computational systems with these capabilities. The first two vignettes explore computational models of skill learning from worked examples, correctness feedback, and verbal instruction. The third vignette investigates computational models of concept formation from natural language corpora. In conclusion, I will discuss future research directions and a broader vision for how cognitive science and cognitive systems research can lead to new AI advancements.