Tag: explanations
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Aligned explanations in neural networks
Aligned explanations in neural networks arXiv:2601.04378v1 Announce Type: cross Abstract: Feature attribution is the dominant paradigm for explaining deep neural networks. However, most existing methods only loosely reflect the model’s prediction-making process, thereby merely white-painting the black box. We argue that explanatory alignment is a key aspect of trustworthiness in prediction tasks: explanations must be…
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Interpretable Model-Aware Counterfactual Explanations for Random Forest
Interpretable Model-Aware Counterfactual Explanations for Random Forest arXiv:2510.27397v1 Announce Type: new Abstract: Despite their enormous predictive power, machine learning models are often unsuitable for applications in regulated industries such as finance, due to their limited capacity to provide explanations. While model-agnostic frameworks such as Shapley values have proved to be convenient and popular, they rarely…
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Performative Validity of Recourse Explanations
Performative Validity of Recourse Explanations arXiv:2506.15366v1 Announce Type: new Abstract: When applicants get rejected by an algorithmic decision system, recourse explanations provide actionable suggestions for how to change their input features to get a positive evaluation. A crucial yet overlooked phenomenon is that recourse explanations are performative: When many applicants act according to their recommendations,…