  The uses of machine learning (ML) have snowballed in recent years. In many
cases, ML models are highly complex, and their operation is beyond the
understanding of human decision-makers. Nevertheless, some uses of ML models
involve high-stakes and safety-critical applications. Explainable artificial
intelligence (XAI) aims to help human decision-makers in understanding the
operation of such complex ML models, thus eliciting trust in their operation.
Unfortunately, the majority of past XAI work is based on informal approaches,
that offer no guarantees of rigor. Unsurprisingly, there exists comprehensive
experimental and theoretical evidence confirming that informal methods of XAI
can provide human-decision makers with erroneous information. Logic-based XAI
represents a rigorous approach to explainability; it is model-based and offers
the strongest guarantees of rigor of computed explanations. However, a
well-known drawback of logic-based XAI is the complexity of logic reasoning,
especially for highly complex ML models. Recent work proposed
distance-restricted explanations, i.e. explanations that are rigorous provided
the distance to a given input is small enough. Distance-restricted
explainability is tightly related with adversarial robustness, and it has been
shown to scale for moderately complex ML models, but the number of inputs still
represents a key limiting factor. This paper investigates novel algorithms for
scaling up the performance of logic-based explainers when computing and
enumerating ML model explanations with a large number of inputs.
