Tootfinder

Impact Assessment Card: Communicating Risks and Benefits of AI Uses
Edyta Bogucka, Marios Constantinides, Sanja \v{S}\'cepanovi\'c, Daniele Quercia
https://arxiv.org/abs/2508.18919

Impact Assessment Card: Communicating Risks and Benefits of AI Uses
Communicating the risks and benefits of AI is important for regulation and public understanding. Yet current methods such as technical reports often exclude people without technical expertise. Drawing on HCI research, we developed an Impact Assessment Card to present this information more clearly. We held three focus groups with a total of 12 participants who helped identify design requirements and create early versions of the card. We then tested a refined version in an online study with 235 p…

CAVEMOVE: An Acoustic Database for the Study of Voice-enabled Technologies inside Moving Vehicles
Nikolaos Stefanakis, Marinos Kalaitzakis, Andreas Symiakakis, Stefanos Papadakis, Despoina Pavlidi
https://arxiv.org/abs/2508.19691

CAVEMOVE: An Acoustic Database for the Study of Voice-enabled Technologies inside Moving Vehicles
In this paper, we present an acoustic database, designed to drive and support research on voiced enabled technologies inside moving vehicles. The recording process involves (i) recordings of acoustic impulse responses, acquired under static conditions to provide the means for modeling the speech and car-audio components (ii) recordings of acoustic noise at a wide range of static and in-motion conditions. Data are recorded with two different microphone configurations, particularly (i) a compact …

How to Make Your Headphones Last: Headphone Care & Maintenance
#MusicianTips

The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms
Pooja S. B. Rao, Sanja \v{S}\'cepanovi\'c, Dinesh Babu Jayagopi, Mauro Cherubini, Daniele Quercia
https://arxiv.org/abs/2508.16672

The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms
We analyzed nearly 460,000 AI model cards from Hugging Face to examine how developers report risks. From these, we extracted around 3,000 unique risk mentions and built the \emph{AI Model Risk Catalog}. We compared these with risks identified by researchers in the MIT Risk Repository and with real-world incidents from the AI Incident Database. Developers focused on technical issues like bias and safety, while researchers emphasized broader social impacts. Both groups paid little attention to fr…

Replaced article(s) found for gr-qc. https://arxiv.org/list/gr-qc/new
[2/2]:
- Relativistic gas accretion onto supermassive black Hole binaries from inspiral through merger
Ennoggi, Campanelli, Zlochower, Noble, Krolik, Cattorini, Kalinani, Mewes, Chabanov, Ji, de Simone

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