Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Buyl, Maarten"'
The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, by enabling fully-automated personalized and interactive content generation at an unprecedented scale. In this paper, we survey the
Externí odkaz:
http://arxiv.org/abs/2411.06837
Autor:
Buyl, Maarten, Rogiers, Alexander, Noels, Sander, Dominguez-Catena, Iris, Heiter, Edith, Romero, Raphael, Johary, Iman, Mara, Alexandru-Cristian, Lijffijt, Jefrey, De Bie, Tijl
Large language models (LLMs) are trained on vast amounts of data to generate natural language, enabling them to perform tasks like text summarization and question answering. These models have become popular in artificial intelligence (AI) assistants
Externí odkaz:
http://arxiv.org/abs/2410.18417
Numerous methods have been implemented that pursue fairness with respect to sensitive features by mitigating biases in machine learning. Yet, the problem settings that each method tackles vary significantly, including the stage of intervention, the c
Externí odkaz:
http://arxiv.org/abs/2409.16965
Publikováno v:
Appl. Sci. 2024, 14(8), 3516
Dynamic Link Prediction (DLP) addresses the prediction of future links in evolving networks. However, accurately portraying the performance of DLP algorithms poses challenges that might impede progress in the field. Importantly, common evaluation pip
Externí odkaz:
http://arxiv.org/abs/2405.17182
KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information. The tool extracts and concisely summarizes key excerpts from parliamentary proceedings, followed by the potential f
Externí odkaz:
http://arxiv.org/abs/2404.17597
Current fairness toolkits in machine learning only admit a limited range of fairness definitions and have seen little integration with automatic differentiation libraries, despite the central role these libraries play in modern machine learning pipel
Externí odkaz:
http://arxiv.org/abs/2310.17256
Web applications where users are presented with a limited selection of items have long employed ranking models to put the most relevant results first. Any feedback received from users is typically assumed to reflect a relative judgement on the utilit
Externí odkaz:
http://arxiv.org/abs/2306.05808
Autor:
Buyl, Maarten, De Bie, Tijl
As the real-world impact of Artificial Intelligence (AI) systems has been steadily growing, so too have these systems come under increasing scrutiny. In response, the study of AI fairness has rapidly developed into a rich field of research with links
Externí odkaz:
http://arxiv.org/abs/2212.06495
Autor:
BUYL, MAARTEN1 maarten.buyl@ugent.be, DE BIE, TIJL2
Publikováno v:
Communications of the ACM. Feb2024, Vol. 67 Issue 2, p48-55. 8p.
Tackling algorithmic discrimination against persons with disabilities (PWDs) demands a distinctive approach that is fundamentally different to that applied to other protected characteristics, due to particular ethical, legal, and technical challenges
Externí odkaz:
http://arxiv.org/abs/2206.06149