2022/05/19 by Ouns El Harzli, Harzli, Ouns El, Bernardo Cuenca Grau +3 · 1 citation
Computer Science · #Explainable Artificial Intelligence (XAI) #Adversarial Robustness in Machine Learning #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.2205.09901
In recent years, there has been increasing interest in explanation methods for neural model predictions that offer precise formal guarantees. These include abductive (respectively, contrastive) methods, which aim to compute minimal subsets of input features that are sufficient for a given prediction to hold (respectively, to change a given prediction). The corresponding decision problems are, however, known to be intractable. In this paper, we investigate whether tractability can be regained by focusing on neural models implementing a monotonic function. Although the relevant decision problems remain intractable, we can show that they become solvable in polynomial time by means of greedy algorithms if we additionally assume that the activation functions are continuous everywhere and differentiable almost everywhere. Our experiments suggest favourable performance of our algorithms.