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Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions

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arXiv 2024年

Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novices. This creates a gap between conveyed information and information that matters to those who are impacted by the system’s decisions, such as domain experts and decision subjects. To address this, we present the "XAI Novice Question Bank," an extension of the XAI Question Bank [60] containing a catalog of information needs from AI novices in two use cases: employment prediction and health monitoring. The catalog covers the categories of data, system context, system usage, and system specifications. We gathered information needs through task-based interviews where participants asked questions about two AI systems to decide on their adoption and received verbal explanations in response. Our analysis showed that participants’ confidence increased after receiving explanations but that their understanding faced challenges. These included difficulties in locating information and in assessing their own understanding, as well as attempts to outsource understanding. Additionally, participants’ prior perceptions of the systems’ risks and benefits influenced their information needs. Participants who perceived high risks sought explanations about the intentions behind a system’s deployment, while those who perceived low risks rather asked about the system’s operation. Our work aims to support the inclusion of AI novices in explainability efforts by highlighting their information needs, aims, and challenges. We summarize our findings as five key implications that can inform the design of future explanations for lay stakeholder audiences. Copyright © 2024, The Authors. All rights reserved.

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

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PraK Tool V3: Enhancing Video Item Search Using Localized Te...

31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making

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arXiv 2023年

作者： Schmude, Timothée Koesten, Laura Möller, Torsten Tschiatschek, Sebastian University of Vienna Faculty of Computer Science Research Network Data Science UniVie Doctoral School Computer Science DoCS Austria University of Vienna Faculty of Computer Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Visualization and Data Analysis Austria University of Vienna Faculty of Computer Science Research Network Data Science Research Group Data Mining and Machine Learning Austria

We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences. We shortly introduce the importance of explaining ADM systems, give a brief overview of approaches drawing from other disciplines to improve explanations, and present the results of our qualitative task-based study incorporating the "six facets of understanding" [24] framework. We close with questions guiding the discussion of how future studies can leverage an interdisciplinary approach. © 2023, CC BY.

关键词： Decision making

PraK Tool: An Interactive Search Tool Based on Video data Services 30th

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PraK Tool: An Interactive Search Tool Based on Video Data S...

30th International Conference on MultiMedia Modeling, MMM 2024

作者： Lokoč, Jakub Vopálková, Zuzana Stroh, Michael Buchmueller, Raphael Schlegel, Udo SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9783031533013

This paper presents a tool relying on data service architecture, where technical details of all VBS datasets are completely hidden behind an abstract stateless data layer. The data services allow independent development of interactive search interfaces and refinement techniques, which is demonstrated by a smart front-end component. The component supports common search features and allows users to exploit content-based statistics for effective filtering. We believe that video data services might be a valuable addition to the open-source VBS toolkit, especially when available for the competition on a shared server with all VBS datasets, extracted features, and meta-data behind. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Video recording

On the Impact of Explanations on Understanding of Algorithmic Decision-Making

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arXiv 2023年

Ethical principles for algorithms are gaining importance as more and more stakeholders are affected by "high-risk" algorithmic decision-making (ADM) systems. Understanding how these systems work enables stakeholders to make informed decisions and to assess the systems’ adherence to ethical values. Explanations are a promising way to create understanding, but current explainable artificial intelligence (XAI) research does not always consider existent theories on how understanding is formed and evaluated. In this work, we aim to contribute to a better understanding of understanding by conducting a qualitative task-based study with 30 participants, including users and affected stakeholders. We use three explanation modalities (textual, dialogue, and interactive) to explain a "high-risk" ADM system to participants and analyse their responses both inductively and deductively, using the "six facets of understanding" framework by Wiggins & McTighe [63]. Our findings indicate that the "six facets" framework is a promising approach to analyse participants’ thought processes in understanding, providing categories for both rational and emotional understanding. We further introduce the "dialogue" modality as a valid explanation approach to increase participant engagement and interaction with the "explainer", allowing for more insight into their understanding in the process. Our analysis further suggests that individuality in understanding affects participants’ perceptions of algorithmic fairness, demonstrating the interdependence between understanding and ADM assessment that previous studies have outlined. We posit that drawing from theories on learning and understanding like the "six facets" and leveraging explanation modalities can guide XAI research to better suit explanations to learning processes of individuals and consequently enable their assessment of ethical values of ADM systems. © 2023, CC BY.

关键词： Decision making

Exploring Uncertainty in Image Segmentation Ensembles

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Exploring Uncertainty in Image Segmentation Ensembles

20th Eurographics/IEEE VGTC Conference on visualization, EuroVis 2018

作者： Fröhler, B. Möller, T. Weissenböck, J. Hege, H.-C. Kastner, J. Heinzl, C. Research Group Computed Tomography University of Applied Sciences Upper Austria Austria Visualization and Data Analysis Research Group Faculty of Computer Science University of Vienna Austria Visual Data Analysis Department Zuse Institute Berlin Germany

ISBN: (纸本)9783038680659

Finding the most accurate image segmentation involves analyzing results from different algorithms or parameterizations. In this work, we identify different types of uncertainty in this analysis that are represented by the results of probabilistic algorithms, by the local variability in the segmentation, and by the variability across the segmentation ensemble. We propose visualization techniques for the analysis of such types of uncertainties in segmentation ensembles. For a global analysis we provide overview visualizations in the image domain as well as in the label space. Our probability probing and scatter plot based techniques facilitate a local analysis. We evaluate our techniques using a case study on industrial computed tomography data. © 2018 The Author(s)

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Responsibilities for receiving and using individual participant data

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Cochrane Evidence Synthesis and Methods

Cochrane Evidence Synthesis and Methods 2023年第9期1卷

作者： Kylie E. Hunter Aidan C. Tan Angela C. Webster Daniel G. Hamilton Adrian Barnett Lee Jones Myra Cheng Salma Fahridin Antonio Laguna-Camacho Sol Libesman Mark Hoffmann Rui Wang Anna Lene Seidler NHMRC Clinical Trials Centre University of Sydney Sydney New South Wales Australia Contribution: Conceptualization Data curation Formal analysis Investigation Methodology Project administration Resources Validation Visualization Writing - original draft Writing - review & editing Contribution: Conceptualization Investigation Methodology Resources Supervision Writing - review & editing MetaMelb Research Group University of Melbourne Melbourne Victoria Australia Melbourne Medical School University of Melbourne Melbourne Victoria Australia Contribution: Investigation Resources Validation Writing - review & editing School of Public Health and Social Work Queensland University of Technology Brisbane Queensland Australia Australian Research Data Commons Canberra Australian Capital Territory Australia School of Medicine Autonomous University of the State of Mexico Toluca Mexico Queensland Cyber Infrastructure Foundation Brisbane Queensland Australia Department of Obstetrics and Gynaecology Monash University Melbourne Victoria Australia Contribution: Conceptualization Data curation Formal analysis Investigation Methodology Project administration Resources Supervision Validation Visualization Writing - review & editing

Background Sharing of individual participant data enhances the value of existing data to generate new evidence and inform decision-making. While there is strong in-principle support for data sharing, in practice study data are often difficult to find, access, and re-use. Currently, there is no consensus statement to guide the data-sharing process. In particular, more guidance is needed on the responsibilities of data recipients for re-using individual participant data. Purpose To determine views on the responsibilities of recipients of study data, and to propose how these responsibilities could be met. Methods A 2-h online focus group was conducted at the 2021 Association for Interdisciplinary Meta-research and Open Science conference. Three example data-sharing scenarios were discussed (evidence synthesis, study reproducibility, and secondary analyses). Notes and audio transcripts were collated using thematic analysis and shared with attendees for further iterative input. Results A purposive sample of 16 conference delegates attended the focus group. Analyses revealed four recurring themes that were synthesized into recommendations. The “privacy and ethics” theme described the need for data recipients to prioritize the protection of participant privacy, and the recommendation to proactively share a secure data management plan and evidence of ethical oversight with the data provider. The “capability and resourcing” theme required recipients to demonstrate sufficient capacity to process and analyze study data. The “recognition and collaboration” theme asserted the responsibility to acknowledge the contributions of data providers and invite them to contribute to the secondary project. Last, the “compliance” theme focused on the responsibility to adhere to local data sharing regulations. Conclusions Successful data sharing and re-use requires cooperation from multiple stakeholders. We identified the responsibilities of recipients of study data to the individual from wh

关键词： data provider data recipient data sharing health research individual participant data reproducibility

Carbazole-Dendronized Luminescent Radicals

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Angewandte Chemie 2023年第28期135卷

作者： Rui Xiaotian Wataru Ota Prof. Tohru Sato Minori Furukori Prof. Yasuo Nakayama Prof. Takuya Hosokai Eri Hisamura Kazuhiro Nakamura Kenshiro Matsuda Dr. Kohei Nakao Prof. Andrew P. Monkman Prof. Ken Albrecht Department of Applied Science for Electronics and Materials Interdisciplinary Graduate School of Engineering Sciences Kyushu University 6-1 Kasuga-Koen Kasuga-shi Fukuoka 816-8580 Japan Contribution: Conceptualization (supporting) Data curation (lead) Formal analysis (lead) Funding acquisition (supporting) Investigation (equal) Visualization (equal) Writing - original draft (lead) MOLFEX Inc. Takano Nishibiraki-cho 34–4 Sakyo-ku Kyoto 606-8103 Japan Contribution: Conceptualization (supporting) Data curation (equal) Formal analysis (equal) Investigation (equal) Methodology (equal) Resources (supporting) Software (supporting) Visualization (supporting) Writing - original draft (supporting) Fukui Institute for Fundamental Chemistry Kyoto University Takano Nishibiraki-cho 34–4 Sakyo-ku Kyoto 606-8103 Japan Department of Molecular Engineering Graduate School of Engineering Kyoto University Nishikyo-ku Kyoto 615-8510 Japan Elements Strategy Initiative for Catalysts & Batteries (ESICB) Kyoto University Kyotodaigaku Katsura Nishikyo-ku Kyoto 615-8245 Japan Contribution: Conceptualization (supporting) Software (supporting) Supervision (equal) Validation (supporting) Writing - review & editing (supporting) Department of Pure and Applied Chemistry Graduate School of Science and Technology Tokyo University of Science 2641 Yamazaki Noda Chiba 278-8510 Japan National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Central 5 1-1-1 Higashi Tsukuba Ibaraki 305-8565 Japan Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Contribution: Supervision (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (supporting) Data curation (supporting) Formal analysis (supporting) Funding acquisition (supporting) Supervision (equal) Writing - review & editing (equal) Institute for Materials Chemistry and Engineering Kyushu University 6-1 Kasuga-Koen Kasuga-shi Fukuoka 816-8580 J

A series of carbazole-dendronized tris(2,4,6-trichlorophenyl)methyl (TTM) radicals have been synthesized. The photophysical properties of dendronized radicals up to the fourth generation were compared systematically to understand how structure–property relationships evolve with generation. The photoluminescence quantum yield (PLQY) was found to increase with the increasing generation, and the fourth generation (G4TTM) in cyclohexane solution showed a PLQY as high as 63 % at a wavelength of 627 nm (in the deep-red region) from the doublet state. The dendron modification strategy also showed a blue-shift of the emission on increasing the generation number, and the photostability was also increased compared to the bare TTM radical.

关键词： Carbazoles Dendrimers Excited Doublet State Luminescencent Materials Stable Radicals

Biomedical image analysis competitions: The state of current participation practice

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking