The user experience of our daily interactions is increasingly shaped with the aid of AI, mostly as the output of recommendation engines. However, it is less common to present users with possibilities to navigate or adapt such output. In this paper we argue that adding such algorithmic controls can be a potent strategy to create explainable AI and to aid users in building adequate mental models of the system. We describe our efforts to create a pattern library for algorithmic controls: the algorithmic affordances pattern library. The library can aid in bridging research efforts to explore and evaluate algorithmic controls and emerging practices in commercial applications, therewith scaffolding a more evidence-based adoption of algorithmic controls in industry. A first version of the library suggested four distinct categories of algorithmic controls: feeding the algorithm, tuning algorithmic parameters, activating recommendation contexts, and navigating the recommendation space. In this paper we discuss these and reflect on how each of them could aid explainability. Based on this reflection, we unfold a sketch for a future research agenda. The paper also serves as an open invitation to the XAI community to strengthen our approach with things we missed so far.
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The evolving landscape of science communication highlights a shift from traditional dissemination to participatory engagement. This study explores Dutch citizens’ perspectives on science communication, focusing on science capital, public engagement, and communication goals. Using a mixed-methods approach, it combines survey data (n = 376) with focus group (n = 66) insights. Findings show increasing public interest in participating in science, though barriers like knowledge gaps persist. Trust-building, engaging adolescents, and integrating science into society were identified as key goals. These insights support the development of the Netherlands’ National Centre of Expertise on Science and Society and provide guidance for inclusive, effective science communication practices.
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In this chapter, the authors elaborate on serious games and playful interactionsin modern scientific practices, and on the way they can engendermutual scientific growth. They use a research-through-design approach, inwhich three possible scenarios and prototypes are studied to envisage thenew role of the public library in practicing science in a changing society.Their conclusion is that the public library of the future should employcitizen science projects that are fun, accessible, malleable, and participatory,so that its new role can focus on offering meaningful informationat the right time in the right place, contextualizing information usingplayful solutions, bringing together communities to share information,and enabling new scientific practices in unexplored fields.
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Algorithmic affordances are defined as user interaction mechanisms that allow users tangible control over AI algorithms, such as recommender systems. Designing such algorithmic affordances, including assessing their impact, is not straightforward and practitioners state that they lack resources to design adequately for interfaces of AI systems. This could be amended by creating a comprehensive pattern library of algorithmic affordances. This library should provide easy access to patterns, supported by live examples and research on their experiential impact and limitations of use. The Algorithmic Affordances in Recommender Interfaces workshop aimed to address key challenges related to building such a pattern library, including pattern identification features, a framework for systematic impact evaluation, and understanding the interaction between algorithmic affordances and their context of use, especially in education or with users with a low algorithmic literacy. Preliminary solutions were proposed for these challenges.
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Video games clearly have great educational potential, both for formal and informal learning, and this avenue is being thoroughly investigated in the psychology and education literature. However, there appears to be a disconnect between social science academic research and the game development sector, in that research and development practices rarely inform each other. This paper presents a two-part analysis of this communicative disconnect based on investigations carried out within the H2020 Gaming Horizons project. The first part regards a literature review that identified the main topics of focus in the social sciences literature on games, as well as the chief recommendations authors express. The second part examines 73 interviews with 30 developers, 14 researchers, 13 players, 12 educators, and 4 policy makers, investigating how they perceived games and gaming. The study highlights several factors contributing to the disconnect: different priorities and dissemination practices; the lag between innovation in the games market and research advancements; low accessibility of academic research; and disproportionate academic focus on serious games compared to entertainment games. The authors suggest closer contact between researchers and developers might be sought by diversifying academic dissemination channels, promoting conferences involving both groups, and developing research partnerships with entertainment game companies.
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Prof. Dr. Dieter Eckstein (1939 - 2021) significantly influenced the global development of dendrochronology and the underlying science of wood biology. Eckstein’s research areas included dendroclimatology, xylogenesis, ecophysiology, and quantitative wood anatomy. His personal and collaborative work continues to improve our understanding of both the natural environment and human cultural development. The techniques he developed and championed resolved long-standing difficulties in the application of tree-ring science to understand both natural processes and human effects on tree and forest development. As importantly, he nurtured and promoted both the careers and the lives of many fellow scholars and students around the world. Here we present a systematic bibliography of more than 280 publications that illustrates the development of tree-ring research in Europe and elsewhere throughout the almost 50 years of Eckstein’s career. Throughout his scientific career, Eckstein pioneered, developed, and promoted research opportunities with his students and co-workers at the University of Hamburg and beyond. His greatest legacy for his students and colleagues, and which we are challenged to continue, is to continue to build the international spirit of a "dendrofamily".
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The research described in this paper provides insights into tools and methods which are used by professional information workers to keep and to manage their personal information. A literature study was carried out on 23 scholar papers and articles, retrieved from the ACM Digital Library and Library and Information Science Abstracts (LISA). The research questions were: - How do information workers keep and manage their information sources? - What aims do they have when building personal information collections? - What problems do they experience with the use and management of their personal collections? The main conclusion from the literature is that professional information workers use different tools and approaches for personal information management, depending on their personal style, the types of information in their collections and the devices which they use for retrieval. The main problem that they experience is that of information fragmentation over different collections and different devices. These findings can provide input for improvement of information literacy curricula in Higher Education. It has been remarked that scholar research and literature on Personal Information Management do not pay a lot of attention to the keeping and management of (bibliographic) data from external documentation. How people process the information from those sources and how this stimulates their personal learning, is completely overlooked. [The original publication is available at www.elpub.net]
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Between 1 March 2021 and 30 April 2023, a consortium (consisting of in the Netherlands: the National Library of the Netherlands (Koninklijke Bibliotheek -KB), The Hague University of Applied Sciences, the Netherlands Institute for Sound and Vision in Hilversum; in Belgium: Media & Learning Association in Leuven and Public Libraries 2030 in Brussels; and in Spain: Fundación Platoniq in Barcelona) carried out an Erasmus+-funded research project on news media literacy among young people. It involved Dutch, Belgian and Spanish young people aged 12-15. The acronym SMILES, which stands for 'innovative methodS for Media & Information Literacy Education involving schools and librarieS', was chosen for the project title. The main goals of the SMILES project are: • Forming pairs between librarians and secondary school teachers in the three European countries, who were empowered through train-the-trainer workshops to teach secondary school students about news media literacy in relation to disinformation; • Helping students use digital technologies more safely and responsibly with a focus on recognising reliable and authentic information versus becoming more resilient to disinformation; • Developing five building blocks serving as teaching materials for Dutch, Belgian and Spanish pupils aged 12-15 with the aim of making them recognise disinformation and making them more resilient against it; • A scientific evaluation of the effectiveness of the implemented lessons through impact measurement using 'pre-knowledge tests' and 'post-knowledge tests'; • A strengthening of existing collaborations and creation of new collaborations between schools and libraries in the three partner countries. The SMILES project was implemented through three work packages. In the first work package, five so-called 'Baseline studies', or literature reviews, were conducted. The focus was on what the different educational approaches in Spain, Belgium and the Netherlands are with regard to disinformation and how these approaches can be linked. Based on these studies, the five building blocks were developed in the second work package. In addition, the teaching pairs were offered the training programme developed by SMILES through a 'train-the-trainer methodology' to safely and responsibly deploy the use of digital media tools during lessons with students. Also, based on the disinformation literature, the knowledge tests were designed to conduct an impact measurement of the train-the-trainer workshops and lessons among the trainers (teaching pairs) and students, respectively. These knowledge tests contained statements on disinformation that were answered correctly or incorrectly by respondents. The number of correctly answered statements prior to the lessons was compared with the number of correctly answered statements after the lessons. In this way, an attempt was made to prove a positive learning effect of the deployed lessons. In the third work package, the results from the pre-knowledge tests and the post-knowledge tests were analysed. In addition to these quantitative analyses, qualitative results were also used to analyse and look at the extent to which the training provided to trainers (teaching pairs) and the lessons with the five building blocks for students proved effective in teaching, recognising and becoming more resilient to disinformation, respectively. In doing so, we also reflect on whether the methodology tested has been effective in the three countries: what are the best practices and where do we see areas for improvement?
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Jo-An Kamp is a lecturer and researcher at Fontys University of Applied Sciences in the Netherlands. She coaches ICT students in the fields of UX, research, (interactive) media, communication, (interaction) design, ethics and innovation. She does research on the impact of technology on humans and society. Jo-An is co-creator of the Technology Impact Cycle Toolkit (www.tict.io), a toolkit designed to make people think and make better decisions about (the implementation of) technology and is a member of the Moral Design Strategy research group.
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Prompt design can be understood similarly to query design, as a prompt aiming to understand cultural dimensions in visual research, forcing the AI to make sense of ambiguity as a way to understand its training dataset and biases ( Niederer, S. and Colombo, G., ‘Visual Methods for Digital Research’). It moves away from prompting engineering and efforts to make “code-like” prompts that suppress ambiguity and prevent the AI from bringing biases to the surface. Our idea is to keep the ambiguity present in the image descriptions like in natural language and let it flow through different stages (degrees) of the broken telephone dynamics. This way we have less control over the result or selection of the ideal result and more questions about the dynamics implicit in the biases present in the results obtained.Different from textual or mathematical results, in which prompt chains or asking the AI to explain how it got the result might be enough, images and visual methods assisted by AI demand new methods to deal with that. Exploring and developing a new approach for it is the main goal of this research project, particularly interested in possible biases and unexplored patterns in AI’s image affordances.How could we detect small biases in describing images and creating based on descriptions when it comes to AI? What exactly do the words written by AI when describing an image stand for? When detecting a ‘human’ or ‘science’, for example, what elements or archetypes are invisible between prompting, and the image created or described?Turning an AI’s image description into a new image could help us to have a glimpse behind the scenes. In the broken telephone game, small misperceptions between telling and hearing, coding and decoding, produce big divergences in the final result - and the cultural factors in between have been largely studied. To amplify and understand possible biases, we could check how this new image would be described by AI, starting a broken telephone cycle. This process could shed light not just into the gap between AI image description and its capacity to reconstruct images using this description as part of prompts, but also illuminate biases and patterns in AI image description and creation based on description.It is in line with previous projects on image clustering and image prompt analysis (see reference links), and questions such as identification of AI image biases, cross AI models analysis, reverse engineering through prompts, image clustering, and analysis of large datasets of images from online image and video-based platforms.The experiment becomes even more relevant in light of the results from recent studies (Shumailov et al., 2024) that show that AI models trained on AI generated data will eventually collapse.To frame this analysis, the proposal from Munn, Magee and Arora (2023) titled Unmaking AI Imagemaking introduces three methodological approaches for investigating AI image models: Unmaking the ecosystem, Unmaking the data and Unmaking the outputs.First, the idea of ecosystem was taken for these authors to describe socio-technical implications that surround the AI models: the place where they have been developed; the owners, partners, or supporters; and their interests, goals, and impositions. “Research has already identified how these image models internalize toxic stereotypes (Birnhane 2021) and reproduce forms of gendered and ethnic bias (Luccioni 2023), to name just two issues” (Munn et al., 2023, p. 2).There are also differences between the different models that currently dominate the market. Although Stable Diffusion seems to be the most open due to its origin, when working with images with this model, biases appear even more quickly than in other models. “In this framing, Stable Diffusion becomes an internet-based tool, which can be used and abused by “the people,” rather than a corporate product, where responsibility is clear, quality must be ensured, and toxicity must be mitigated” (Munn et al., 2023, p. 5).To unmaking the data, it is important to ask ourselves about the source and interests for the extraction of the data used. According to the description of their project “Creating an Ad Library Political Observatory”, “This project aims to explore diverse approaches to analyze and visualize the data from Meta’s ad library, which includes Instagram, Facebook, and other Meta products, using LLMs. The ultimate goal is to enhance the Ad Library Political Observatory, a tool we are developing to monitor Meta’s ad business.” That is to say, the images were taken from political advertising on the social network Facebook, as part of an observation process that seeks to make evident the investments in advertising around politics. These are prepared images in terms of what is seen in the background of the image, the position and posture of the characters, the visible objects. In general, we could say that we are dealing with staged images. This is important since the initial information that describes the AI is in itself a representation, a visual creation.
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