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2nd ACM Conference on Information Technology for Social Good, GoodIT 2022

作者： Buddemeyer, Amanda Nwogu, Jennifer Solyst, Jaemarie Walker, Erin Nkrumah, Tara Ogan, Amy Hatley, Leshell Stewart, Angela School of Computing and Information Learning Research and Development Center University of Pittsburgh United States Human Computer Interaction Institute Carnegie Mellon University United States Center for Gender Equity in Science and Technology Arizona State University United States Uplift Inc.

ISBN: (纸本)9781450392846

Language is an act of identity, but AI has no identity other than that which its creators assign it. Technology creators who do not fully consider how identity information is encoded in AI dialogue risk creating representational harms that negatively impact users' interactions with the technology, particularly young users from groups marginalized in STEM education. One solution is to engage young users in Participatory Design of dialogue, pushing power away from the researcher and toward the young users. This positions users as technology creators who understand AI language as a technosocial phenomenon and who have their own perspective about how identity is best portrayed in AI language. In this work in progress paper, we provide initial impressions from a Youth Advisory Group (YAG) with 12 middle-school-aged learners of color who are all female or non-binary. © 2022 Owner/Author.

关键词： Engineering education

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CL-MIL: Multi-Instance Learning on Whole-Slide Images with Self-Supervised Contrastive Learning 10

CL-MIL: Multi-Instance Learning on Whole-Slide Images with S...

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10th International Conference on computer and Communications, ICCC 2024

作者： Xiu, Pengfei Liu, Dongying Wang, Sheng Wang, Hongliang Wang, Wei Li, Shun Shenyang Institute of Computing Technology Chinese Academy of Science Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shengjing Hospital of China Medical University Department of Gastroenterology Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

Histopathological examination of tissue slides is considered the gold standard for clinical cancer diagnosis. However, it heavily relies on pathologists' subjective judgment and involves substantial workload. Existing convolutional neural network (CNN)-based whole slide image (WSI) diagnostic methods suffer from high annotation costs and complex data acquisition processes. Therefore, this study proposes a weakly supervised multiple instance learning method based on self-supervised contrastive learning (CL-MIL) for WSI classification. By integrating self-supervised contrastive learning strategies, leveraging data augmentation through H&E stain separation, and introducing causal intervention via backdoor adjustment to mitigate dataset bias, our approach significantly enhances feature representation and classification performance in pathological images. Experimental results on the Camelyon16 and TCGA lung cancer datasets demonstrate that our method outperforms other comparable WSI classification models across various evaluation metrics, achieving higher diagnostic efficiency and accuracy. Additionally, our method exhibits good interpretability, enabling precise localization of pathological regions and providing robust technical support for clinical applications. © 2024 IEEE.

关键词： Self-supervised learning

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Sociotechnical AI Governance: Challenges and Opportunities for HCI

Sociotechnical AI Governance: Challenges and Opportunities f...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Kevin Feng, K.J. Pang, Rock Yuren Kuo, Tzu-Sheng Winecoff, Amy Tseng, Emily Widder, David Gray Suresh, Harini Reinecke, Katharina Zhang, Amy X. University of Washington Seattle WA United States Paul G. Allen School of Computer Science & Engineering University of Washington Seattle WA United States Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh PA United States Center for Democracy & Technology Washington DC United States Microsoft Research New York NY United States Cornell Tech New York NY United States Brown University Providence RI United States

ISBN: (纸本)9798400713958

Rapid advancements in and adoption of frontier AI systems have amplified the need for AI governance measures across the public sector, academia, and industry. Prior work in technical AI governance has proposed agendas for governing technical components in AI development, such as data, models, and compute. However, recent calls for more sociotechnical approaches recognize the critical role of social infrastructures surrounding technical ones in shaping governance decisions and efforts. While scholars and practitioners have advocated for sociotechnical AI governance, concrete research directions in this area are only beginning to emerge. This workshop aims to gather the expertise of researchers in HCI and adjacent disciplines to chart promising paths forward for sociotechnical AI governance. To make problems in this area more tangible, we outline four core governance challenges for contributions: anticipating high-priority risks to address with governance, identifying where to focus governance efforts and who should lead those efforts, designing appropriate interventions and tools to implement governance actions in practice, and evaluating the effectiveness of these interventions and tools in context. Through papers, panel discussions, keynotes, and collaborative drafting of a research agenda, this workshop will build community and empower actionable efforts to tackle AI governance through a sociotechnical lens. © 2025 Copyright held by the owner/author(s).

关键词： AI governance human-AI interaction responsible AI sociotechnical methods

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CounselAR: Exploring How AR Filters Facilitate Online Psychotherapy In the Wild With South Korean Young Adults 25

CounselAR: Exploring How AR Filters Facilitate Online Psycho...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Noh, Hayoun Chishti, Salman Muin Kayser Jo, Hyunah Newhouse, Nikki Kim, Songi Van Kleek, Max Kang, Younah Department of Computer Science University of Oxford Oxford United Kingdom Yonsei University Seoul South Korea Irihs Nuffield Department of Primary Care Health Sciences University of Oxford Oxford United Kingdom Graduate School of Logistics Inha University Incheon South Korea Information and Interaction Design Yonsei University Seoul South Korea

ISBN: (纸本)9798400713941

In a society where mental health issues are prevalent, engagement with psychotherapy remains low due to stigma and accessibility barriers. Telepsychotherapy offers a potential solution but holds challenges, including difficulties in encouraging open self-disclosure and ease of access. In this paper, we introduce CounselAR, an augmented reality (AR)-mediated therapy service designed to facilitate one-on-one therapy sessions by allowing both client and therapist to use AR filters to maintain varying degrees of anonymity. Through a six-week field deployment involving nine clients and four therapists, we explored how AR-mediated therapy might support psychotherapy from both the clients' and therapists' perspectives. The results illustrate the potential role of AR filters in enhancing self-disclosure, building rapport, and lowering entry barriers to psychotherapy. Drawing on these findings, we discuss the nuanced role of AR filters and the implications of leveraging AR in psychotherapy. © 2025 Copyright held by the owner/author(s).

关键词： AR Field Deployment Study HCI Mental Health Psychotherapy Young Adults

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Encountered-Type Tabletop Haptic Display for Objects On-Demand in Virtual Environments

Encountered-Type Tabletop Haptic Display for Objects On-Dema...

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IEEE International Conference on Robotics and Biomimetics

作者： Yannick Jonetzko Oscar Ariza Susanne Schmidt Niklas Fiedler Jianwei Zhang Department of Informatics Group Technical Aspects of Multimodal Systems (TAMS) Universität Hamburg Germany Department of Informatics Group Human Computer Interaction (HCI) Universität Hamburg Germany

This paper presents the design and capabilities of a custom-built Encountered-Type Haptic Display. With an X-Y-Z-Yaw plotter mechanism below the tabletop and four permanent magnets in the end-effector, the device can manipulate multiple objects in three dimensions on top of the table. Four hall effect sensors in the end-effector are used to compensate the friction of the display and increase the positioning accuracy of objects to 0.5 mm. With an end-effector speed of 66.6 cm/s, objects can be placed in a workspace of 47.2×26 cm. The device is built for human-computer interaction scenarios in virtual reality (VR) and augmented reality (AR) to provide haptic feedback on-demand. Due to the design, arbitrary object shapes and materials can be presented to the user within the table’s workspace. The usability and performance of the table are evaluated with a user study.

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When Does an Individual Accept Misinformation? An Extended Investigation Through Cognitive Modeling

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Computational Brain and Behavior 2022年第2期5卷 244-260页

作者： Borukhson, David Lorenz-Spreen, Philipp Ragni, Marco Department for Computer Science University of Freiburg Freiburg Germany Center for Adaptive Rationality Max Planck Institute for Human Development Berlin Germany Predictive Analytics Technical University Chemnitz Chemnitz Germany

A new phenomenon is the spread and acceptance of misinformation and disinformation on an individual user level, facilitated by social media such as Twitter. So far, state-of-the-art socio-psychological theories and cognitive models focus on explaining how the accuracy of fake news is judged on average, with little consideration of the individual. In this paper, a breadth of core models are comparatively assessed on their predictive accuracy for the individual decision maker, i.e., how well can models predict an individual’s decision before the decision is made. To conduct this analysis, it requires the raw responses of each individual and the implementation and adaption of theories to predict the individual’s response. Building on methods formerly applied on smaller and more limited datasets, we used three previously collected large datasets with a total of 3794 participants and searched for, analyzed and refined existing classical and heuristic modeling approaches. The results suggest that classical reasoning, sentiment analysis models and heuristic approaches can best predict the “Accept” or “Reject” response of a person, headed by a model put together from research by Jay Van Bavel, while other models such as an implementation of “motivated reasoning” performed worse. Further, hybrid models that combine pairs of individual models achieve a significant increase in performance, pointing to an adaptive toolbox. © 2022, The Author(s).

关键词： Fake news detection Hybrid cognitive models Predictive modeling Socio-psychological theories

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Failure Explanation in Privacy-Sensitive Contexts: An Integrated Systems Approach

Failure Explanation in Privacy-Sensitive Contexts: An Integr...

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IEEE International Workshop on Robot and human Communication (ROMAN)

作者： Sihui Li Sriram Siva Terran Mott Tom Williams Hao Zhang Neil Dantam Department of Computer Science Colorado School of Mines Human-Centered Robotics Lab University of Massachusetts Amherst

In this paper, we explore how robots can properly explain failures during navigation tasks with privacy concerns. We present an integrated robotics approach to generate visual failure explanations, by combining a language-capable cognitive architecture (for recognizing intent behind commands), an object- and location-based context recognition system (for identifying the locations of people and classifying the context in which those people are situated) and an infeasibility proof-based motion planner (for explaining planning failures on the basis of contextually mediated privacy concerns). The behavior of this integrated system is validated using a series of experiments in a simulated medical environment.

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Collaborative Framework for circRNA-Disease Associations Prediction using Dual Variational Graph

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IEEE Transactions on Big Data 2025年

作者： Liu, Changchun Wang, Lei Zhao, Bowei Wei, Mengmeng Li, Yang Lu, Mianshuo Wei, Yu Liang, Sizhe The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Key Lab of Human-machine Interaction and Intelligent Decision Guangxi Acad emy of Science Nanning530007 China The School of Computer Science and Information Engineer ing Hefei University of Technology Hefei230601 China The College of Computer and Information Science School of Software University of Chinese Academy of Sciences Urumqi830011 China

Many experiments have shown that circular RNA (circRNA) can act as biomarkers for complex diseases and play significant regulatory roles in multiple pathological processes. However, most circRNA-disease associations remain unknown, and discovering these associations through biological experimental approach is expensive and time-consuming. Taking into account the shortcomings of current methods, we introduce a new collaborative framework that utilizes multi-heterogeneous graphs, along with variational graph auto-encoders (VGAE) to predict associations between circRNA and diseases. First, we build multi-similarity networks using various biological attributes of circRNA and diseases, and integrate these similarity networks. Two subnetworks are constructed from association matrix and combined similarity network, which included a circRNA-based network and a disease-based network. We then employ random walk with restart and Singular Value Decomposition, for feature extraction from the similarity matrix. Finally, we use collaborative framework to predict the circRNA-disease association scores based on the two subnetworks. We integrate the two score matrices to obtain a final prediction scoring matrix. Using 5-fold cross-validation on the CircR2Disease dataset, our model achieved an AUC score of 0.9828 and an AUPR score of 0.9820. Additionally, among the top 30 highest-scoring circRNA-disease association pairs, 26 associations have already been validated. Our model shows strong performance and can accurately predict associations between circRNA and diseases, according to experimental results. © 2015 IEEE. All rights reserved.

关键词： Variational techniques

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Region-based Saliency Explanations on the Recognition of Facial Genetic Syndromes 8

Region-based Saliency Explanations on the Recognition of Fac...

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8th Machine Learning for Healthcare Conference, MLHC 2023

作者： Sümer, Ömer Waikel, Rebekah L. Ledgister Hanchard, Suzanna E. Duong, Dat Krawitz, Peter Conati, Cristina Solomon, Benjamin D. André, Elisabeth Human-Centered Artificial Intelligence University of Augsburg Augsburg Germany Medical Genetics Branch National Human Genome Research Institute BethesdaMD United States Institute for Genomic Statistics and Bioinformatics University of Bonn Bonn Germany Department of Computer Science University of British Columbia BC Canada

Deep neural networks in computer vision have shown remarkable progress in recognizing facial genetic syndromes. Many genetic syndromes are difficult to detect, even for experienced clinicians, and computer-aided phenotyping can accelerate clinical diagnosis. High-stakes clinical tasks using deep learning, as in clinical genetics, require human understandable explanations for model decisions. Saliency methods are used to explain DNN predictions in various image analysis domains but have yet to be studied in facial genetics. The syndromic features of most genetic conditions are often localized to areas like the eyes, nose, and mouth. In this paper, to summarize the contribution of key facial regions to a specific disease prediction, we propose a face region relevance score that can be applied to any saliency method. We also investigate how prior knowledge, namely human phenotype ontology and DNN model explanations, align. Quantitative experiments are performed on a new database containing over 3,500 images of 11 rare facial syndromes, a healthy control group, and an additional test set of 171 facial images, whose respective facial phenotypes are labeled by clinicians. Current saliency methods are good at capturing dysmorphism in particular regions (parts of the face), but they may not completely capture all the relevant features in a given person or condition. Our study indicates which saliency explanations and face regions are more consistent with the phenotypes of specific genetic syndromes and could be used in large-scale clinical evaluations. © 2023 Ö. Sümer, R.L. Waikel, S.E.L. Hanchard, D. Duong, P. Krawitz, C. Conati, B.D. Solomon & E. André.

关键词： Deep neural networks

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Multi-modal 3D image registration using interactive voxel grid deformation and rendering

Multi-modal 3D image registration using interactive voxel gr...

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2022 Eurographics Workshop on Visual Computing for Biology and Medicine, EG VCBM 2022

作者： Richard, T. Chastagnier, Y. Szabo, V. Chalard, K. Summa, B. Thiery, J.-M. Boubekeur, T. Faraj, N. LIRMM University of Montpellier France Institute of Human Genetics University of Montpellier France Department of Computer Science Tulane University United States Adobe Research Paris France

ISBN: (纸本)9783038681779

We introduce a novel multi-modal 3D image registration framework based on 3D user-guided deformation of both volume's shape and intensity values. Being able to apply deformations in 3D gives access to a wide new range of interactions allowing for the registration of images from any acquisition method and of any organ, complete or partial. Our framework uses a state of the art 3D volume rendering method for real-time feedback on the registration accuracy as well as the image deformation. We propose a novel methodological variation to accurately display 3D segmented voxel grids, which is a requirement in a registration context for visualizing a segmented atlas. Our pipeline is implemented in an open-source software (available via GitHub) and was directly used by biologists for registration of mouse brain model autofluorescence acquisition on the Allen Brain Atlas. The latter mapping allows them to retrieve regions of interest properly identified on the segmented atlas in acquired brain datasets and therefore extract only high-resolution images of those areas, avoiding the creation of images too large to be processed. © 2022 The Author(s) © 2022 The Eurographics Association.

关键词： Open source software

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