arXiv subjects

Anders Sundnes Løvlie

Publications and source records attributed to Anders Sundnes Løvlie.

At least 19 recordsLinked to original sources

arXiv2026

The GenAI Catch-22: Use of Generative Artificial Intelligence in Norwegian Newsrooms During the 2025 Parliamentary Election

The increasing use of Generative Artificial Intelligence (GenAI) in journalism raises concerns about possible detrimental effects both on journalism and its democratic function. We explore these risks through a case study of GenAI in Norwegian Newsrooms during the 2025 parliamentary election campaign. Based on interviews with managers and journalists over a ten-month period, we analyse how ambitious visions fared in the face of technological and practical challenges. We highlight the risk of an internal threat stemming from the journalists' own use of AI, contrasting the dominant focus on external disinformation threats. We show how newsroom managers shared sociotechnical imaginaries resulting in unrealistically optimistic beliefs about the capabilities of the technology and the pace of development, leading to plans for audience-facing GenAI services collapsing and giving way to more mundane uses of GenAI tools internally in the newsrooms. Furthermore, we identify a vulnerability in the newsroom's resilience against GenAI influence: a GenAI Catch-22. In order to monitor the GenAI tools and prevent errors and undue influence, newsrooms rely on human expertise. But by using GenAI extensively, the newsrooms risk a deterioration of human expertise, preventing them from monitoring the GenAI systems adequately.

cs.CY↗

arXiv2026

If These Walls Could Talk: Critical Play with Large Language Models in Museums

Large Language Models (LLMs) are increasingly being used in museums to as role playing chatbots which let visitors talk to simulated versions of people and artefacts from the past. While such installations can be playful and engaging, they are also problematic because LLMs cannot be trusted to speak truthfully. I identify a fundamental dilemma for the use of LLMs in museum chatbots: LLMs cannot be trusted to tell the truth, and efforts to make them more reliable may ruin that which is attractive about the bots in the first place - their ability to engage in life-like conversation. In response, I propose designing for critical play with LLM-based bots: Designing for playful interactions with bots that are unreliable but still able to represent the past in an adequate and engaging manner - as fictional characters representing historical narratives, styles of discourse, diverse perspectives, humor and satire.

cs.HC↗

arXiv2025

Using a Large Language Model as Design Material for an Interactive Museum Installation

We present a work in progress that explores using a Large Language Model (LLM) as a design material for an interactive museum installation. LLMs offer the possibility of creating chatbots that can facilitate dynamic and human-like conversation, engaging in a form of role play to bring historical persons to life for visitors. However, LLMs are prone to producing misinformation, which runs counter to museums' core mission to educate the public. We use Research-through-Design to explore some approaches to navigating this dilemma through rapid prototyping and evaluation and propose some directions for further research. We suggest that designers may shape interactions with the chatbot to emphasize personal narratives and role play rather than historical facts or to intentionally highlight the unreliability of the chatbot outputs to provoke critical reflection.

cs.HC↗

arXiv2024

LayerShuffle: Enhancing Robustness in Vision Transformers by Randomizing Layer Execution Order

Due to their architecture and how they are trained, artificial neural networks are typically not robust toward pruning or shuffling layers at test time. However, such properties would be desirable for different applications, such as distributed neural network architectures where the order of execution cannot be guaranteed or parts of the network can fail during inference. In this work, we address these issues through a number of training approaches for vision transformers whose most important component is randomizing the execution order of attention modules at training time. With our proposed approaches, vision transformers are capable to adapt to arbitrary layer execution orders at test time assuming one tolerates a reduction (about 20\%) in accuracy at the same model size. We analyse the feature representations of our trained models as well as how each layer contributes to the models prediction based on its position during inference. Our analysis shows that layers learn to contribute differently based on their position in the network. Finally, we layer-prune our models at test time and find that their performance declines gracefully. Code available at https://github.com/matfrei/layershuffle.

cs.CV↗

arXiv2024

AI-generated art perceptions with GenFrame -- an image-generating picture frame

Image-generation models are changing how we express ourselves in visual art. However, what people think of AI-generated art is still largely unexplored, especially compared to traditional art. In this paper, we present the design of an interactive research product, GenFrame - an image-generating picture frame that appears as a traditional painting but offers the viewer the agency to modify the depicted painting. In the current paper, we report on a study where we deployed the GenFrame in a traditional art museum and interviewed visitors about their views on AI art. When provoked by AI-generated art, people need more of the artist's backstory and emotional journey to make the artwork commensurate with traditional art. However, generative AI-enabled interactive experiences open new ways of engaging with art when a turn of a dial can modify art styles or motifs on a painting. A demo can be seen here: https://youtu.be/1rhW4fazaBY.

cs.HC↗

arXiv2024

Fooling Contrastive Language-Image Pre-trained Models with CLIPMasterPrints

Models leveraging both visual and textual data such as Contrastive Language-Image Pre-training (CLIP), are the backbone of many recent advances in artificial intelligence. In this work, we show that despite their versatility, such models are vulnerable to what we refer to as fooling master images. Fooling master images are capable of maximizing the confidence score of a CLIP model for a significant number of widely varying prompts, while being either unrecognizable or unrelated to the attacked prompts for humans. The existence of such images is problematic as it could be used by bad actors to maliciously interfere with CLIP-trained image retrieval models in production with comparably small effort as a single image can attack many different prompts. We demonstrate how fooling master images for CLIP (CLIPMasterPrints) can be mined using stochastic gradient descent, projected gradient descent, or blackbox optimization. Contrary to many common adversarial attacks, the blackbox optimization approach allows us to mine CLIPMasterPrints even when the weights of the model are not accessible. We investigate the properties of the mined images, and find that images trained on a small number of image captions generalize to a much larger number of semantically related captions. We evaluate possible mitigation strategies, where we increase the robustness of the model and introduce an approach to automatically detect CLIPMasterPrints to sanitize the input of vulnerable models. Finally, we find that vulnerability to CLIPMasterPrints is related to a modality gap in contrastive pre-trained multi-modal networks. Code available at https://github.com/matfrei/CLIPMasterPrints.

cs.CV↗

arXiv2024

Using Tangible Interaction to Design Musicking Artifacts for Non-musicians

This paper presents a Research through Design exploration of the potential for using tangible interactions to enable active music experiences - musicking - for non-musicians. We present the Tubularium prototype, which aims to help non-musicians play music without requiring any initial skill. We present the initial design of the prototype and the features implemented in order to enable music-making by non-musicians, and offer some reflections based on observations of informal initial user explorations of the prototype.

cs.HC↗

arXiv2024

Algorithmic Ways of Seeing: Using Object Detection to Facilitate Art Exploration

This Research through Design paper explores how object detection may be applied to a large digital art museum collection to facilitate new ways of encountering and experiencing art. We present the design and evaluation of an interactive application called SMKExplore, which allows users to explore a museum's digital collection of paintings by browsing through objects detected in the images, as a novel form of open-ended exploration. We provide three contributions. First, we show how an object detection pipeline can be integrated into a design process for visual exploration. Second, we present the design and development of an app that enables exploration of an art museum's collection. Third, we offer reflections on future possibilities for museums and HCI researchers to incorporate object detection techniques into the digitalization of museums.

cs.HC↗

arXiv2024

Machine Learning Processes as Sources of Ambiguity: Insights from AI Art

Ongoing efforts to turn Machine Learning (ML) into a design material have encountered limited success. This paper examines the burgeoning area of AI art to understand how artists incorporate ML in their creative work. Drawing upon related HCI theories, we investigate how artists create ambiguity by analyzing nine AI artworks that use computer vision and image synthesis. Our analysis shows that, in addition to the established types of ambiguity, artists worked closely with the ML process (dataset curation, model training, and application) and developed various techniques to evoke the ambiguity of processes. Our finding indicates that the current conceptualization of ML as a design material needs to reframe the ML process as design elements, instead of technical details. Finally, this paper offers reflections on commonly held assumptions in HCI about ML uncertainty, dependability, and explainability, and advocates to supplement the artifact-centered design perspective of ML with a process-centered one.

cs.HC↗

arXiv2023

Subtle Sound Design: Designing for experience blend in a historic house museum

In this article, we present and discuss a user-study prototype, developed for Bakkehuset historic house museum in Copenhagen. We examine how the prototype - a digital sound installation - can expand visitors' experiences of the house and offer encounters with immaterial cultural heritage. Historic house museums often hold back on utilizing digital communication tools inside the houses, since a central purpose of this type of museum is to preserve an original environment. Digital communication tools however hold great potential for facilitating rich encounters with cultural heritage and in particular with the immaterial aspects of museum collections and their histories. In this article we present our design steps and choices, aiming at subtly and seamlessly adding a digital dimension to a historic house. Based on qualitative interviews, we evaluate how the sound installation at Bakkehuset is sensed, interpreted, and used by visitors as part of their museum experience. In turn, we shed light on the historic house museum as a distinct design context for designing hybrid visitor experiences and point to the potentials of digital communication tools in this context.

cs.HC↗

arXiv2023

Stairway to heaven: designing for an embodied experience with satellite data

This paper explores the design of an interactive installation in a science center which facilitates an embodied experience with satellite data. This addresses a central concern for experience design in museums, which is the question of how to integrate technologies well in the visitor experience, sometimes referred to as "experience blend". We present the design and evaluation of a visualization triggered by movement in a physical staircase to let visitors explore data about satellites at different orbits. The evaluation demonstrates strong experience blend, and points towards similar design opportunities for other institutions interested in finding new uses for mundane pathways through their buildings.

cs.HC↗

arXiv2023

"How trustworthy is this research?" Designing a Tool to Help Readers Understand Evidence and Uncertainty in Science Journalism

This article reports on a Research through Design study exploring how to design a tool for helping readers of science journalism understand the strength and uncertainty of scientific evidence in news stories about health science, using both textual and visual information. A central aim has been to teach readers about criteria for assessing scientific evidence, in particular in order to help readers differentiate between science and pseudoscience. Working in a research-in-the-wild collaboration with a website for popular science, the study presents the design and evaluation of the Scientific Evidence Indicator, which uses metadata about scientific publications to present an assessment of evidence strength to the readers. Evaluations of the design demonstrate some success in helping readers recognize whether studies have undergone scientific peer review or not, but point to challenges in facilitating a more in-depth understanding. Insights from the study point to a potential for developing similar tools aimed at journalists rather than directly at audiences.

cs.DL↗

arXiv2022

Sensitive Pictures: Emotional Interpretation in the Museum

Museums are interested in designing emotional visitor experiences to complement traditional interpretations. HCI is interested in the relationship between Affective Computing and Affective Interaction. We describe Sensitive Pictures, an emotional visitor experience co-created with the Munch art museum. Visitors choose emotions, locate associated paintings in the museum, experience an emotional story while viewing them, and self-report their response. A subsequent interview with a portrayal of the artist employs computer vision to estimate emotional responses from facial expressions. Visitors are given a souvenir postcard visualizing their emotional data. A study of 132 members of the public (39 interviewed) illuminates key themes: designing emotional provocations; capturing emotional responses; engaging visitors with their data; a tendency for them to align their views with the system's interpretation; and integrating these elements into emotional trajectories. We consider how Affective Computing can hold up a mirror to our emotions during Affective Interaction.

cs.HC↗

arXiv2022

The Scientific Evidence Indicator for Popular Science News

To what extent can news media help in providing more credible information about science? This is the core challenge for the Science Evidence Indicator (SEI) project, a collaboration between the Danish popular news website videnskab.dk and the authors of this paper. Looking specifically at medical science news, we aim to provide a transparent assessment of the scientific sources behind a story. This entails identifying some of the criteria that scientists use to assess research, and making it accessible and understandable for readers. We address the following research question: How can we communicate the quality of scientific publications in health science to a non-expert audience? Our goal is to make the assessments understandable for the youngest part of the website's target audience: high school students from age 16 and upwards.

cs.DL↗

arXiv2021

Improving Object Detection in Art Images Using Only Style Transfer

Despite recent advances in object detection using deep learning neural networks, these neural networks still struggle to identify objects in art images such as paintings and drawings. This challenge is known as the cross depiction problem and it stems in part from the tendency of neural networks to prioritize identification of an object's texture over its shape. In this paper we propose and evaluate a process for training neural networks to localize objects - specifically people - in art images. We generate a large dataset for training and validation by modifying the images in the COCO dataset using AdaIn style transfer. This dataset is used to fine-tune a Faster R-CNN object detection network, which is then tested on the existing People-Art testing dataset. The result is a significant improvement on the state of the art and a new way forward for creating datasets to train neural networks to process art images.

cs.CV↗

arXiv2021

Playing games with Tito: Designing hybrid museum experiences for critical play

This article brings together two distinct, but related perspectives on playful museum experiences: Critical play and hybrid design. The article explores the challenges involved in combining these two perspectives, through the design of two hybrid museum experiences that aimed to facilitate critical play with/in the collections of the Museum of Yugoslavia and the highly contested heritage they represent. Based on reflections from the design process as well as feedback from test users we describe a series of challenges: Challenging the norms of visitor behaviour, challenging the role of the artefact, and challenging the curatorial authority. In conclusion we outline some possible design strategies to address these challenges.

cs.HC↗

arXiv2020

We Dare You: A Lifecycle Study of a Substitutional Reality Installation in a Museum Space

In this article, we present a lifecycle study of We Dare You, a Substitutional Reality (SR) installation that combines visual and tactile stimuli. The installation is set up in a center for architecture, and invites visitors to explore its facade while playing with vertigo, in a visual Virtual Reality (VR) environment that replicates the surrounding physical space of the installation. Drawing on an ethnographic approach, including observations and interviews, we researched the exhibit from its opening, through the initial months plagued by technical problems, its subsequent success as a social and playful installation, on to its closure, due to COVID-19, and its subsequent reopening. Our findings explore the challenges caused by both the hybrid nature of the installation, as well as the visitor' playful use of the installation which made the experience social and performative - but also caused some problems. We also discuss the problems We Dare You faced in light of hygiene demands due to COVID-19. The analysis contrasts the design processes and expectations of stakeholders with the audience's playful appropriation, which led the stakeholders to see the installation as both a success and a failure. Evaluating the design and redesign through use on behalf of visitors, we argue that an approach that further opens up the post-production experience to a process of continuous redesign based on the user input - what has been termed "design-after-design" - could facilitate the design of similar experiences in the museum and heritage sector, supporting a participatory agenda in the design process, and helping to resolve the tension between stakeholders' expectations and visitors' playful appropriations.

cs.HC↗

arXiv2020

Designing for Interpersonal Museum Experiences

What does the age of participation look like from the perspective of a museum visitor? Arguably, the concept of participative experiences is already so deeply ingrained in our culture that we may not even think about it as participation. Museum visitors engage in a number of activities, of which observing the exhibits is only one part. Since most visitors come to the museum together with someone else, they spend time and attention on the people they came with, and often the needs of the group are given priority over individual preferences. How can museums tap into these activities - and make themselves relevant to visitors? In this chapter we will try to approach this constructively, as a design opportunity. Could it be productive for the museum to consider itself not only as a disseminator of knowledge, but also as the facilitator of participative activities between visitors? In what follows, we will outline a range of practical design projects that serve as examples of this approach. These projects were part of the European Union funded Horizon2020 project GIFT, a cross-disciplinary collaboration between researchers, artists, designers and many international museums and heritage organisations, exploring the concept of interpersonal museum experiences (see https://gifting.digital/). What the projects have in common is that they build on visitors co-creating and sharing their own narratives in the museum context. We suggest that these projects demonstrate a spectrum of possibilities: From experiences that take place almost without any museum involvement, to those that give museums a role in curating these narratives.

cs.HC↗