  The application of Artificial Intelligence (AI) tools in different domains
are becoming mandatory for all companies wishing to excel in their industries.
One major challenge for a successful application of AI is to combine the
machine learning (ML) expertise with the domain knowledge to have the best
results applying AI tools. Domain specialists have an understanding of the data
and how it can impact their decisions. ML experts have the ability to use
AI-based tools dealing with large amounts of data and generating insights for
domain experts. But without a deep understanding of the data, ML experts are
not able to tune their models to get optimal results for a specific domain.
Therefore, domain experts are key users for ML tools and the explainability of
those AI tools become an essential feature in that context. There are a lot of
efforts to research AI explainability for different contexts, users and goals.
In this position paper, we discuss interesting findings about how ML experts
can express concerns about AI explainability while defining features of an ML
tool to be developed for a specific domain. We analyze data from two brainstorm
sessions done to discuss the functionalities of an ML tool to support
geoscientists (domain experts) on analyzing seismic data (domain-specific data)
with ML resources.
