References

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Cynthia Rudin (2019)

Nature Machine Intelligence, 1(5), 206-215.

DOI: https://doi.org/10.1038/s42256-019-0048-x

Abstract. Argues that post-hoc explanations of black-box models are fundamentally unreliable for high-stakes decisions and that practitioners should instead design intrinsically interpretable models whose decision logic can be verified directly.

Tags: ethics explainability interpretability

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