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From data revolution to data narratives
Guilherme Cavalcante Silva
Science as Culture·2023
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View paper →Brooding: In 2026, I Resolve to Friction-Maxx
Kathryn Jezer-Morton
The Cut·2026
In this edition of Brooding, Kathryn Jezer-Morton argues that in, the face of technologies that sell us escapism at every turn, we need to build up tolerance for “inconvenience’ and start friction-maxxing.
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View paper →Where Knowledge Begins? Generative Search, Information Literacy, and the Problem of Friction
Leslie Allison, Tiffany DeRewal
Critical AI·2024·1 citations
<jats:title>Abstract</jats:title>
<jats:p>This article explores the rise of generative AI and large language model (LLM) tools for internet search and their potential impact on student information seeking behavior. Reviewing what is known about best search practices, the essay identifies a schism between AI companies’ vision of search and information science's understanding of humane search environments. It argues that generative search tools fail to create ethical spaces for search, leading to dangerous territories for knowledge production. Specifically, the article discusses “friction” as a critical concept where the field of information science and AI development philosophies diverge. Whereas information science views friction as a valuable and often necessary component to search, AI companies view friction as a problem to eliminate. It is this mismatch between corporate AI philosophies and known best practices for search that, this essay argues, renders current LLM and generative AI search tools fundamentally incompatible with ethical processes of knowledge production.</jats:p>
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View paper →A typology of platform power and its regulation
Inge Graef, Friso Bostoen
Information, Communication & Society·2025·1 citations
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View paper →Experimental narratives: A comparison of human crowdsourced storytelling and AI storytelling
Nina Beguš
Humanities and Social Sciences Communications·2024·19 citations
<jats:title>Abstract</jats:title><jats:p>The paper proposes a framework that combines behavioral and computational experiments employing fictional prompts as a novel tool for investigating cultural artifacts and social biases in storytelling both by humans and generative AI. The study analyzes 250 stories authored by crowdworkers in June 2019 and 80 stories generated by GPT-3.5 and GPT-4 in March 2023 by merging methods from narratology and inferential statistics. Both crowdworkers and large language models responded to identical prompts about creating and falling in love with an artificial human. The proposed experimental paradigm allows a direct and controlled comparison between human and LLM-generated storytelling. Responses to the Pygmalionesque prompts confirm the pervasive presence of the Pygmalion myth in the collective imaginary of both humans and large language models. All solicited narratives present a scientific or technological pursuit. The analysis reveals that narratives from GPT-3.5 and particularly GPT-4 are more progressive in terms of gender roles and sexuality than those written by humans. While AI narratives with default settings and no additional prompting can occasionally provide innovative plot twists, they offer less imaginative scenarios and rhetoric than human-authored texts. The proposed framework argues that fiction can be used as a window into human and AI-based collective imaginary and social dimensions.</jats:p>
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View paper →The Impact of Friction on Customer Value Experience in E-Commerce Design
Aigerim Togatay
Digital service design often focuses on seamless and frictionless customer journeys. However, recent studies argue that not all forms of friction negatively impact customer experience by providing evidence that some thoughtfully designed friction can increase consumer value. This thesis examines how different levels of experiential friction affect the customer value experience in e-commerce environments. Based on customer experience, cognitive reflectivity, emotional valence, and the value co-creation and co-destruction framework, this study investigates the effects of low, moderate, and high friction levels in an e-commerce setting. An experiment exposed participants to three simulated checkout journeys, which were later evaluated with a questionnaire that assessed their experience in terms of perceived effort, type of reflection, and emotional response. According to the experiment findings, low friction creates a smooth but shallow customer value experience. In comparison, moderate friction stimulates meaningful cognitive engagement without emotionally overwhelming users, supporting value co-creation. Finally, high friction leads to frustration and emotional decline in customers. This research contributes to the customer experience approach by providing empirical evidence for reflectivity as a mechanism linking friction and value experience. Friction is a deliberate design tool that enhances customer value experience rather than being entirely eliminated.
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View paper →Introduction to the special issue on time and HCI
Amon Rapp, William Odom, Larissa Pschetz, Daniela Petrelli
Human–Computer Interaction·2021·20 citations
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View paper →‘Written in the Style of’: ChatGPT and the Literary Canon · Issue 7.4, Fall 2025
Gabi Kirilloff, Claudia Carroll, Zeina Daboul, Arianwyn Frank, Razi Khan, Marielle Hinrichs-Morrow, Rebecca Weingart
Harvard Data Science Review·2025
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View paper →Key Considerations When Designing a Social Media-Based Diary Study
Ellie Atayee-Bennett
International Journal of Qualitative Methods·2025·1 citations
<jats:p>Qualitative data collection conducted on social media platforms is a recent methodological innovation with only a handful of studies exploring this approach. Even fewer explore the potential of social media platforms for conducting diary methods. This article highlights the key considerations a researcher must reflect upon when designing a social media-based diary study focused on everyday lived experience. It argues that due consideration must be afforded to the platform choice, design and structure of the diary, ethical considerations, cultural considerations, open communication, and strategies to improve participant engagement. In so doing, the researcher can enhance the quality of their study and gain better insight into their research topic. This article is based on a research study which explored the everyday lived experiences of 36 UK-based faith vegans, and which utilized private Facebook and WhatsApp groups to obtain topic-focused diary data from participants, such as photos of food, reflections on different experiences and events, and group discussions.</jats:p>
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View paper →Are self-explanations from Large Language Models faithful?
Andreas Madsen, Sarath Chandar, Siva Reddy
arXiv·2024
Instruction-tuned Large Language Models (LLMs) excel at many tasks and will even explain their reasoning, so-called self-explanations. However, convincing and wrong self-explanations can lead to unsupported confidence in LLMs, thus increasing risk. Therefore, it's important to measure if self-explanations truly reflect the model's behavior. Such a measure is called interpretability-faithfulness and is challenging to perform since the ground truth is inaccessible, and many LLMs only have an inference API. To address this, we propose employing self-consistency checks to measure faithfulness. For example, if an LLM says a set of words is important for making a prediction, then it should not be able to make its prediction without these words. While self-consistency checks are a common approach to faithfulness, they have not previously been successfully applied to LLM self-explanations for counterfactual, feature attribution, and redaction explanations. Our results demonstrate that faithfulness is explanation, model, and task-dependent, showing self-explanations should not be trusted in general. For example, with sentiment classification, counterfactuals are more faithful for Llama2, feature attribution for Mistral, and redaction for Falcon 40B.
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View paper →Currently Reading(1)
A Comprehensive Survey on Automatic Knowledge Graph Construction
Lingfeng Zhong, Jia Wu, Qian Li, Hao Peng, Xindong Wu
arXiv·2023
Automatic knowledge graph construction aims to manufacture structured human knowledge. To this end, much effort has historically been spent extracting informative fact patterns from different data sources. However, more recently, research interest has shifted to acquiring conceptualized structured knowledge beyond informative data. In addition, researchers have also been exploring new ways of handling sophisticated construction tasks in diversified scenarios. Thus, there is a demand for a systematic review of paradigms to organize knowledge structures beyond data-level mentions. To meet this demand, we comprehensively survey more than 300 methods to summarize the latest developments in knowledge graph construction. A knowledge graph is built in three steps: knowledge acquisition, knowledge refinement, and knowledge evolution. The processes of knowledge acquisition are reviewed in detail, including obtaining entities with fine-grained types and their conceptual linkages to knowledge graphs; resolving coreferences; and extracting entity relationships in complex scenarios. The survey covers models for knowledge refinement, including knowledge graph completion, and knowledge fusion. Methods to handle knowledge evolution are also systematically presented, including condition knowledge acquisition, condition knowledge graph completion, and knowledge dynamic. We present the paradigms to compare the distinction among these methods along the axis of the data environment, motivation, and architecture. Additionally, we also provide briefs on accessible resources that can help readers to develop practical knowledge graph systems. The survey concludes with discussions on the challenges and possible directions for future exploration.
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View paper →Papers Read(13)
Digital interface design and power: Friction, threshold, transition
James Ash, Ben Anderson, Rachel Gordon, Paul Langley
Environment and Planning D: Society and Space·2018·77 citations
<jats:p> This paper draws upon the example of High-Cost Short-Term Credit products accessed via digital interfaces and devices to examine practices of interface design and the operation of digitally mediated power. Utilising interviews with High-Cost Short-Term Credit website designers and users of these products, the paper shows how these interfaces are designed and tested to manage frictions: practical, affective or emotional contestations that interrupt or stop users from applying for these products and entering into credit and debt. We suggest that the key role of interface design is to manage these frictions by guiding action in such a way to minimise negative affective states at key thresholds of the application process. The management of friction is enabled by practices of data-driven design, where the contingency of human response is engineered through analytics in order to increase rates of application. Working through the example of High-Cost Short-Term Credit, the paper complicates a notion of control as a smooth or automatic operation of power, instead emphasising the necessity of both continuity and discontinuity as key to modulating action in a digital age. To understand the specificity of interface interactions and move beyond existing work on control, we offer a vocabulary of friction, thresholds and transitions. </jats:p>
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View paper →Client-centered detached modular housing: natural language processing-enabled design recommender system
Inhan Kim, Jihye Shin, Syed Haseeb Shah, Saddiq Ur Rehman
Journal of Computational Design and Engineering·2024·4 citations
AbstractDesigning modular housing is a complex task that necessitates a thorough understanding of the diverse needs of clients in terms of both design aesthetics and floor plan layout. Furthermore, adhering to design for manufacture and assembly (DfMA) principles adds to the complexity, as these are essential modular home requirements. Traditional construction methods frequently fail to meet the specific needs of both clients and DfMA, potentially resulting in suboptimal design solutions. Incorporating client requirements during the design phase necessitates the use of an effective system and framework to reduce changes in subsequent project stages. Existing literature lacks a suitable approach, particularly in the context of modular housing. To address this gap, this paper introduces an artificial intelligence–building information modeling recommender system (RS) for detached modular housing design. The system processes client requirements entered as text utilizing the Word2vec algorithm with the GloVe dataset, refined through transfer learning using surveyed client data of housing needs. The system recommends three distinct modular building design alternatives sourced from a building information modeling models database using cosine and Euclidean similarity functions. A sensitivity analysis ensures that client needs are considered fairly, increasing the robustness of the RS. By incorporating natural language processing, this system transforms the construction industry by making initial designs more client-centric compared with traditional methods. Furthermore, it promotes improved collaboration among clients, design, and construction teams, reducing modifications to design in later stages of construction.
★ 5.0 (1)
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