TL;DR
It is suggested that the notion of the experimenters’ regress (ER) in scientific instruments is useful to understand the situation of XAI, and a new variant of ER is identified in the design and application of XAI algorithms: the validity of XAI algorithms and the workings of black-box models cannot be secured without assuming the other.
The NeuroRule knowledge distillation framework that results in explainable rule-sets from neural network models is established, establishing that black-box neural network models can be made explainable and therefore useful in real-world applications where trustworthiness is paramount.
It is concluded that explainability is a necessary, though not sufficient, condition for trustworthy Al, and concrete directions for future research are outlined, including standardised benchmarks, human-centred evaluation, and explainability for large generative models.
Abstract Artificial intelligence (AI) is rapidly transforming computational biology. However, closer scrutiny reveals that many reported results do not hold under rigorous evaluation. This paper identifies prevalent, recurring issues in the field, including narrow datasets, hidden confounders that make supposedly indep...
Explanations of machine learning models are usually judged by criteria that are hard to compare. We propose a simpler test: if an explanation really describes how a model uses its features, it should be possible to rebuild the model's predictions from it. We turn each explanation into a predictor by reading each featur...
Cross-seed dictionary stability prioritisation finds interpretable latents using about 4.4 times fewer latent evaluations each, and at 5.2 times lower measured cost, while recovering over half of them, and the external check shows the surfaced motifs are significantly enriched for their claimed annotations.
Explainable Artificial Intelligence (XAI) has become essential for ensuring transparency, accountability, and trust in modern AI systems, particularly as machine learning and deep learning models grow increasingly complex. The survey is the in-depth analysis of the foundations of the XAI, its key definitions, the conce...
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