Zobrazeno 1 - 10
of 3 537
pro vyhledávání: '"A, Quercia"'
State-of-the-art computer vision tasks, like monocular depth estimation (MDE), rely heavily on large, modern Transformer-based architectures. However, their application in safety-critical domains demands reliable predictive performance and uncertaint
Externí odkaz:
http://arxiv.org/abs/2409.17085
Co-designing an AI Impact Assessment Report Template with AI Practitioners and AI Compliance Experts
In the evolving landscape of AI regulation, it is crucial for companies to conduct impact assessments and document their compliance through comprehensive reports. However, current reports lack grounding in regulations and often focus on specific aspe
Externí odkaz:
http://arxiv.org/abs/2407.17374
Urban parks provide significant health benefits by offering spaces and facilities for various recreational and leisure activities. However, the capacity of specific park spaces and elements to foster health remains underexamined. Traditional studies
Externí odkaz:
http://arxiv.org/abs/2407.15770
Autor:
Bogucka, Edyta, Constantinides, Marios, Velazquez, Julia De Miguel, Šćepanović, Sanja, Quercia, Daniele, Gvirtz, Andrés
Today's visualization tools for conveying the risks and benefits of AI technologies are largely tailored for those with technical expertise. To bridge this gap, we have developed a visualization that employs narrative patterns and interactive element
Externí odkaz:
http://arxiv.org/abs/2407.15685
Translational research, especially in the fast-evolving field of Artificial Intelligence (AI), is key to converting scientific findings into practical innovations. In Responsible AI (RAI) research, translational impact is often viewed through various
Externí odkaz:
http://arxiv.org/abs/2407.15647
Responsible AI design is increasingly seen as an imperative by both AI developers and AI compliance experts. One of the key tasks is envisioning AI technology uses and risks. Recent studies on the model and data cards reveal that AI practitioners str
Externí odkaz:
http://arxiv.org/abs/2407.12454
Integrating Artificial Intelligence (AI) into mobile and wearables offers numerous benefits at individual, societal, and environmental levels. Yet, it also spotlights concerns over emerging risks. Traditional assessments of risks and benefits have be
Externí odkaz:
http://arxiv.org/abs/2407.09322
AI regulations are expected to prohibit machine learning models from using sensitive attributes during training. However, the latest Natural Language Processing (NLP) classifiers, which rely on deep learning, operate as black-box systems, complicatin
Externí odkaz:
http://arxiv.org/abs/2407.01697
Autor:
Yfantidou, Sofia, Spathis, Dimitris, Constantinides, Marios, Vakali, Athena, Quercia, Daniele, Kawsar, Fahim
Self-supervised learning (SSL) has become the de facto training paradigm of large models, where pre-training is followed by supervised fine-tuning using domain-specific data and labels. Despite demonstrating comparable performance with supervised met
Externí odkaz:
http://arxiv.org/abs/2406.02361
Much of the research in social computing analyzes data from social media platforms, which may inherently carry biases. An overlooked source of such bias is the over-representation of WEIRD (Western, Educated, Industrialized, Rich, and Democratic) pop
Externí odkaz:
http://arxiv.org/abs/2406.02090