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4th Workshop on Reducing Online Misinformation through Credible information Retrieval, ROMCIR 2024

作者： Hornig, Benedikt Pera, Maria Soledad Scholtes, Jan Maastricht University Maastricht Netherlands Web Information Systems Delft University of Technology Delft Netherlands

With this paper, we delve into the problem of misinformation propagation in the video recommendation domain, focusing on top-N recommendation algorithms (RAs). We evaluate a broad spectrum of RAs to probe their ability to minimize misinformation recommendations while optimizing the RAs for overall performance. The results of an empirical exploration conducted using a suite of Top-N RAs and a video recommendation dataset [1] show that certain RAs excel in both performance and misinformation handling, while others struggle in mitigating misinformation. Our findings emphasize the potential of neighbourhood-based, neural, and other advanced collaborative filtering (CF) approaches in combating misinformation and contributing to more responsible recommender systems. Inspired by our findings, we propose investigating hybrid RAs and exploring specific features influencing misinformation recommendations, to further enhance the understanding and effectiveness of mitigating misinformation in recommendation systems. © 2024 Copyright for this paper by its authors.

关键词： Recommender systems

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Seeking information with a More Knowledgeable Other

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Interactions 2023年第1期30卷 70-73页

作者： Ekstrand, Michael D. Pera, Maria Soledad Wright, Katherine Landau Department of Computer Science Boise State University United States Web Information Systems Group Delft University of Technology Software Technology Department Netherlands

This forum provides a space to engage with the challenges of designing for intelligent algorithmic experiences. We invite articles that tackle the tensions between research and practice when integrating AI and UX design. We welcome interdisciplinary debate, artful critique, forward-looking research, case studies of AI in practice, and speculative design explorations. - - Juho Kim and Henriette Cramer, Editors © 2023 ACM.

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Gesticulate for Health’s Sake! Understanding the Use of Gestures as an Input Modality for Microtask Crowdsourcing 10th

Gesticulate for Health’s Sake! Understanding the Use of Ges...

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10th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022

作者： Allen, Garrett Hu, Andrea Gadiraju, Ujwal Web Information Systems Delft University of Technology Netherlands

ISBN: (纸本)9781577358787

Human input is pivotal in building reliable and robust artificial intelligence systems. By providing a means to gather diverse, high-quality, representative, and cost-effective human in put on demand, micro task crowdsourcing marketplace shave thrived. Despite the unmistakable benefits available from online crowd work, the lack of health provisions and safeguards, along with existing work practices threatens the sustainability of this paradigm. Prior work has investigated worker engagement and mental health, yet no such investigations into the effects of crowd work on the physical health of workers have been undertaken. Crowd workers complete their work in various sub-optimal work environments, often using a conventional input modality of a mouse and keyboard. The repetitive nature of micro task crowdsourcing can lead to stress-related injuries, such as the well-documented carpal tunnel syndrome. It is known that stretching exercise scan help reduce injuries and discomfort in office workers. Gestures, the act of using the body intentionally to affect the behavior of an intelligent system, can serve as both stretches and an alternative form of input for micro tasks. To better understand the usefulness of the dual-purpose in put modality of ergonomically-informed gestures across different crowd sourced micro tasks, we carried out a controlled 2 × 3 between-subjects study (N=294). Considering the potential benefits of gestures as an input modality, our results suggesta real trade-off between worker accuracy in exchange for potential short to long-term health benefits. © 2022, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Crowdsourcing

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Quantum Data Management: From Theory to Opportunities 40

Quantum Data Management: From Theory to Opportunities

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Hai, Rihan Hung, Shih-Han Feld, Sebastian Delft University of Technology Web Information Systems Group Netherlands Institute of Information Science Academia Sinica Taiwan Delft University of Technology Quantum Machine Learning Group Netherlands

ISBN: (纸本)9798350317152

Quantum computing has emerged as a transformative tool for future data management. Classical problems in database domains, including query optimization, data integration, and transaction management, have recently been addressed using quantum computing techniques. This tutorial aims to establish the theoretical foundation essential for enhancing methodologies and practical implementations in this line of research. Moreover, this tutorial takes a forward-looking approach by delving into recent strides in quantum internet technologies and the nonlocality theory. We aim to shed light on the uncharted territory of future data systems tailored for the quantum internet. © 2024 IEEE.

关键词： information management

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Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages 25

Mind the Gap! Choice Independence in Using Multilingual LLMs...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing systems

作者： Shreyan Biswas Alexander Erlei Ujwal Gadiraju Web Information Systems Technical University of Delft Delft Zuid Holland Netherlands Department of Economics University of Goettingen Goettingen Germany Web Information Systems Delft University of Technology Delft Zuid Holland Netherlands

ISBN: (纸本)9798400713941

Recent advances in generative AI have precipitated a proliferation of novel writing assistants. These systems typically rely on multilingual large language models (LLMs), providing globalized workers the ability to revise or create diverse forms of content in different languages. However, there is substantial evidence indicating that the performance of multilingual LLMs varies between languages. Users who employ writing assistance for multiple languages are therefore susceptible to disparate output quality. Importantly, recent research has shown that people tend to generalize algorithmic errors across independent tasks, violating the behavioral axiom of choice independence. In this paper, we analyze whether user utilization of novel writing assistants in a charity advertisement writing task is affected by the AI's performance in a second language. Furthermore, we quantify the extent to which these patterns translate into the persuasiveness of generated charity advertisements, as well as the role of peoples' beliefs about LLM utilization for their donation choices. Our results provide evidence that writers who engage with an LLM-based writing assistant violate choice independence, as prior exposure to a Spanish LLM reduces subsequent utilization of an English LLM. While these patterns do not affect the aggregate persuasiveness of the generated advertisements, people's beliefs about the source of an advertisement (human versus AI) do. In particular, Spanish-speaking female participants who believed that they read an AI-generated advertisement strongly adjusted their donation behavior downwards. Furthermore, people are generally not able to adequately differentiate between human-generated and LLM-generated ads. Our work has important implications on the design, development, integration, and adoption of multilingual LLMs as assistive agents - particularly in writing tasks. © 2025 Copyright held by the owner/author(s).

关键词： Human-AI interaction

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How does information Pollution Challenge Children’s Right to information Access? 3

How does Information Pollution Challenge Children’s Right t...

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3rd Workshop on Reducing Online Misinformation through Credible information Retrieval, ROMCIR 2023

作者： Landoni, Monica Murgia, Emiliana Huibers, Theo Pera, Maria Soledad Università della Svizzera Italiana Lugano Switzerland Università di Genova Genova Italy University of Twente Twente Netherlands Web Information Systems Delft University of Technology Delft Netherlands

Informed by existing literature, in addition to lessons learned from ongoing research work pertaining to online information seeking, in this contribution, we discuss our view of how information pollution affects a critical yet understudied user group: children. We first highlight the need to take into account the unique characteristics of children’s search context, which can be defined in terms of various factors, from children’s age, abilities, skills, and cognitive development to the fuzzy line separating learning and fun. We then describe the importance of good design in assisting children in the different roles they play as searchers so that they can recognize and distinguish harmful and helpful content. Lastly, we discuss guidelines for effectively engaging teachers, parents, and children in the design, introduction, and use of search tools to support young users not only in accessing the information available online but also taking advantage of and learning safely from it. The focus on children not only helps us move forward to help a target group but, more importantly, it is a great starting point for further investigating a broad range of information pollution issues. © 2023 Copyright for this paper by its authors.

关键词： information retrieval

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Towards a multi-stakeholder value-based assessment framework for algorithmic systems 22

Towards a multi-stakeholder value-based assessment framework...

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5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Yurrita, Mireia Murray-Rust, Dave Balayn, Agathe Bozzon, Alessandro Human Information Communication Design Delft University of Technology Netherlands Web Information Systems Tu Delft Netherlands Knowledge and Intelligence Design Tu Delft Netherlands

ISBN: (纸本)9781450393522

In an effort to regulate Machine Learning-driven (ML) systems, current auditing processes mostly focus on detecting harmful algorithmic biases. While these strategies have proven to be impactful, some values outlined in documents dealing with ethics in ML-driven systems are still underrepresented in auditing processes. Such unaddressed values mainly deal with contextual factors that cannot be easily quantified. In this paper, we develop a value-based assessment framework that is not limited to bias auditing and that covers prominent ethical principles for algorithmic systems. Our framework presents a circular arrangement of values with two bipolar dimensions that make common motivations and potential tensions explicit. In order to operationalize these high-level principles, values are then broken down into specific criteria and their manifestations. However, some of these value-specific criteria are mutually exclusive and require negotiation. As opposed to some other auditing frameworks that merely rely on ML researchers' and practitioners' input, we argue that it is necessary to include stakeholders that present diverse standpoints to systematically negotiate and consolidate value and criteria tensions. To that end, we map stakeholders with different insight needs, and assign tailored means for communicating value manifestations to them. We, therefore, contribute to current ML auditing practices with an assessment framework that visualizes closeness and tensions between values and we give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders. © 2022 Owner/Author.

关键词： ML development and deployment pipeline

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Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant 25

Plan-Then-Execute: An Empirical Study of User Trust and Team...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing systems

作者： Gaole He Gianluca Demartini Ujwal Gadiraju Delft University of Technology Delft Netherlands School of Information Technology and Electrical Engineering University of Queensland Brisbane Australia Web Information Systems Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400713941

Since the explosion in popularity of ChatGPT, large language models (LLMs) have continued to impact our everyday lives. Equipped with external tools that are designed for a specific purpose (e.g., for flight booking or an alarm clock), LLM agents exercise an increasing capability to assist humans in their daily work. Although LLM agents have shown a promising blueprint as daily assistants, there is a limited understanding of how they can provide daily assistance based on planning and sequential decision making capabilities. We draw inspiration from recent work that has highlighted the value of g'LLM-modulo' setups in conjunction with humans-in-the-loop for planning tasks. We conducted an empirical study (N = 248) of LLM agents as daily assistants in six commonly occurring tasks with different levels of risk typically associated with them (e.g., flight ticket booking and credit card payments). To ensure user agency and control over the LLM agent, we adopted LLM agents in a plan-then-execute manner, wherein the agents conducted step-wise planning and step-by-step execution in a simulation environment. We analyzed how user involvement at each stage affects their trust and collaborative team performance. Our findings demonstrate that LLM agents can be a double-edged sword - (1) they can work well when a high-quality plan and necessary user involvement in execution are available, and (2) users can easily mistrust the LLM agents with plans that seem plausible. We synthesized key insights for using LLM agents as daily assistants to calibrate user trust and achieve better overall task outcomes. Our work has important implications for the future design of daily assistants and human-AI collaboration with LLM agents. © 2025 Copyright held by the owner/author(s).

关键词： Human-AI Collaboration

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Quantum Data Management: From Theory to Opportunities

Quantum Data Management: From Theory to Opportunities

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International Conference on Data Engineering

作者： Rihan Hai Shih-Han Hung Sebastian Feld Web Information Systems Group Delft University of Technology Institute of Information Science Academia Sinica Quantum Machine Learning Group Delft University of Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

关键词： Quantum computing Databases Query processing Quantum mechanics Data integration Tutorials Data engineering

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AltRecSys: A Workshop on Alternative, Unexpected, and Critical Ideas in Recommendation 24

AltRecSys: A Workshop on Alternative, Unexpected, and Critic...

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Proceedings of the 18th ACM Conference on Recommender systems

作者： Michael Ekstrand Maria Soledad Pera Alan Said Information Science Drexel University USA Web Information Systems - Software Technology TU Delft Netherlands Applied IT University of Gothenburg Sweden

ISBN: (纸本)9798400705052

The AltRecsys workshop, held in conjunction with the 18th edition of the ACM Conference on Recommender systems (RecSys) in Bari, Italy, provides a platform for highlighting “alternative” work in recommender systems. Modeled after *** and the CRAFT sessions at the FAccT conference, AltRecSys offers a space to discuss interesting, preliminary, offbeat, unexpected, and critical ideas in recommender systems that do not (yet) fit well into the kinds of publications and formats for the main conference or traditional workshops. This workshop is not a venue to showcase research advances. Instead, it is envisioned as a forum where researchers, (industry) practitioners, and other associated stakeholders can exchange ideas and together identify areas of study and new questions to expand the discussions and research agendas of the RecSys community in future years. The call for contributions and the workshop sessions are centered around the question “what are the vital questions, needs, or opportunities that the RecSys community is currently overlooking?”

关键词： alternative ideas

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