Zobrazeno 1 - 10
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pro vyhledávání: '"Martens, David"'
Publikováno v:
Histoires Littèraires. jul-sep2023, Vol. 24 Issue 95, p145-156. 12p.
Autor:
Hinns, James, Martens, David
The rise of deep learning in image classification has brought unprecedented accuracy but also highlighted a key issue: the use of 'shortcuts' by models. Such shortcuts are easy-to-learn patterns from the training data that fail to generalise to new d
Externí odkaz:
http://arxiv.org/abs/2405.15661
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
In many AI applications today, the predominance of black-box machine learning models, due to their typically higher accuracy, amplifies the need for Explainable AI (XAI). Existing XAI approaches, such as the widely used SHAP values or counterfactual
Externí odkaz:
http://arxiv.org/abs/2309.17057
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
Despite the success of complex machine learning algorithms, mostly justified by an outstanding performance in prediction tasks, their inherent opaque nature still represents a challenge to their responsible application. Counterfactual explanations su
Externí odkaz:
http://arxiv.org/abs/2306.06506
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
Counterfactual explanations are increasingly used as an Explainable Artificial Intelligence (XAI) technique to provide stakeholders of complex machine learning algorithms with explanations for data-driven decisions. The popularity of counterfactual e
Externí odkaz:
http://arxiv.org/abs/2304.12667
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