ArXiv Preprint
The recent spike in certified Artificial Intelligence (AI) tools for
healthcare has renewed the debate around adoption of this technology. One
thread of such debate concerns Explainable AI and its promise to render AI
devices more transparent and trustworthy. A few voices active in the medical AI
space have expressed concerns on the reliability of Explainable AI techniques
and especially feature attribution methods, questioning their use and inclusion
in guidelines and standards. Despite valid concerns, we argue that existing
criticism on the viability of post-hoc local explainability methods throws away
the baby with the bathwater by generalizing a problem that is specific to image
data. We begin by characterizing the problem as a lack of semantic match
between explanations and human understanding. To understand when feature
importance can be used reliably, we introduce a distinction between feature
importance of low- and high-level features. We argue that for data types where
low-level features come endowed with a clear semantics, such as tabular data
like Electronic Health Records (EHRs), semantic match can be obtained, and thus
feature attribution methods can still be employed in a meaningful and useful
way.
Giovanni Cinà, Tabea E. Röber, Rob Goedhart, Ş. İlker Birbil
2023-01-05
