Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Goethals, Sofie"'
Access to resources strongly constrains the decisions we make. While we might wish to offer every student a scholarship, or schedule every patient for follow-up meetings with a specialist, limited resources mean that this is not possible. When deploy
Externí odkaz:
http://arxiv.org/abs/2406.01290
This study examines the use of Large Language Models (LLMs) for retrieving factual information, addressing concerns over their propensity to produce factually incorrect "hallucinated" responses or to altogether decline to even answer prompt at all. S
Externí odkaz:
http://arxiv.org/abs/2403.09148
Autor:
Goethals, Sofie, Calders, Toon
Artificial Intelligence (AI) finds widespread application across various domains, but it sparks concerns about fairness in its deployment. The prevailing discourse in classification often emphasizes outcome-based metrics comparing sensitive subgroups
Externí odkaz:
http://arxiv.org/abs/2401.13391
Our online lives generate a wealth of behavioral records -'digital footprints'- which are stored and leveraged by technology platforms. This data can be used to create value for users by personalizing services. At the same time, however, it also pose
Externí odkaz:
http://arxiv.org/abs/2312.15000
Artificial Intelligence (AI) systems are increasingly used in high-stakes domains of our life, increasing the need to explain these decisions and to make sure that they are aligned with how we want the decision to be made. The field of Explainable AI
Externí odkaz:
http://arxiv.org/abs/2306.13885
In eXplainable Artificial Intelligence (XAI), counterfactual explanations are known to give simple, short, and comprehensible justifications for complex model decisions. However, we are yet to see more applied studies in which they are applied in rea
Externí odkaz:
http://arxiv.org/abs/2305.10069
Algorithms used by organizations increasingly wield power in society as they decide the allocation of key resources and basic goods. In order to promote fairer, juster, and more transparent uses of such decision-making power, explainable artificial i
Externí odkaz:
http://arxiv.org/abs/2304.06483
Black-box machine learning models are being used in more and more high-stakes domains, which creates a growing need for Explainable AI (XAI). Unfortunately, the use of XAI in machine learning introduces new privacy risks, which currently remain large
Externí odkaz:
http://arxiv.org/abs/2210.12051
Autor:
Najim, Omar, Huizing, Manon, Papadimitriou, Konstantinos, Trinh, Xuan Bich, Pauwels, Patrick, Goethals, Sofie, Zwaenepoel, Karen, Peterson, Kevin, Weyler, Joost, Altintas, Sevilay, van Dam, Peter, Tjalma, Wiebren
Publikováno v:
In Cancer Treatment and Research Communications 2019 19
Publikováno v:
Machine Learning; May2024, Vol. 113 Issue 5, p3111-3142, 32p