In the quest for Explainable Artificial Intelligence (XAI) one of the
questions that frequently arises given a decision made by an AI system is,
``why was the decision made in this way?'' Formal approaches to explainability
build a formal model of the AI system and use this to reason about the
properties of the system. Given a set of feature values for an instance to be
explained, and a resulting decision, a formal abductive explanation is a set of
features, such that if they take the given value will always lead to the same
decision. This explanation is useful, it shows that only some features were
used in making the final decision. But it is narrow, it only shows that if the
selected features take their given values the decision is unchanged. It's
possible that some features may change values and still lead to the same
decision. In this paper we formally define inflated explanations which is a set
of features, and for each feature of set of values (always including the value
of the instance being explained), such that the decision will remain unchanged.
Inflated explanations are more informative than abductive explanations since
e.g they allow us to see if the exact value of a feature is important, or it
could be any nearby value. Overall they allow us to better understand the role
of each feature in the decision. We show that we can compute inflated
explanations for not that much greater cost than abductive explanations, and
that we can extend duality results for abductive explanations also to inflated
explanations.

本文提出了 inflated explanation 的概念，通过使用 set of features，在考虑到 feature 不同取值的情况下，能够更好地理解 each feature 在 decision-making 中的角色，同时证明可以用较低代价计算它。