# Interpretable Machine Learning with Python

* Source Data:
  * What factors are appropriate & fair for this context?
  * What level of 'accuracy' is fair for this decision?
* Training Data:
  * What historical reference points are appropriate & fair for this decision?
  * What unjust biases exist in the construction of the historical data?

Kirsten Martin (<https://doi.org/10.1007/s10551-018-3921-3>) Ethical Implications and Accountability of Algorithms

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{% embed url="<https://link.springer.com/article/10.1007/s10551-018-3921-3>" %}

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