Local explanation methods highlight the input tokens that have a considerable
impact on the outcome of classifying the document at hand. For example, the
Anchor algorithm applies a statistical analysis of the sensitivity of the
classifier to changes in the token. Aggregating local explanations over a
dataset provides a global explanation of the model. Such aggregation aims to
detect words with the most impact, giving valuable insights about the model,
like what it has learned in training and which adversarial examples expose its
weaknesses. However, standard aggregation methods bear a high computational
cost: a na\"ive implementation applies a costly algorithm to each token of each
document, and hence, it is infeasible for a simple user running in the scope of
a short analysis session. % We devise techniques for accelerating the global
aggregation of the Anchor algorithm. Specifically, our goal is to compute a set
of top-$k$ words with the highest global impact according to different
aggregation functions. Some of our techniques are lossless and some are lossy.
We show that for a very mild loss of quality, we are able to accelerate the
computation by up to 30$\times$, reducing the computation from hours to
minutes. We also devise and study a probabilistic model that accounts for noise
in the Anchor algorithm and diminishes the bias toward words that are frequent
yet low in impact.

对 Anchor 算法进行加速计算和降低偏见的概率模型