Explainability in High-Stakes Decisions: A Perspective on What Attribution Methods Are Actually For
DOI:
https://doi.org/10.5281/zenodo.22660614Keywords:
Explainability in High-Stakes Decisions; Applied machine learning; Evidence synthesis; Production deployment; MLOpsAbstract
Explainability in High-Stakes Decisions is an area of active development in applied machine learning, with evidence that has
accumulated unevenly across its component questions. We review the current evidence base for explainability in high-stakes
decisions, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has converged
and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We conclude
that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance.
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