Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.¶
Rudin, C. (2019). Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nature Machine Intelligence, 1, 206-215.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Rashomon Effect
- The discipline the prime imposes follows directly: a claim like "the model learned that feature X drives the outcome" is not licensed by the data, because another equally accurate model in the Rashomon set ignores X entirely.
This sourceArgues that with a Rashomon set of equally accurate models, a claim that the model 'learned' a given feature is not licensed by the data alone.
- The discipline the prime imposes follows directly: a claim like "the model learned that feature X drives the outcome" is not licensed by the data, because another equally accurate model in the Rashomon set ignores X entirely.
Verification¶
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