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You're reading from  Interpretable Machine Learning with Python - Second Edition

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Published inOct 2023
PublisherPackt
ISBN-139781803235424
Edition2nd Edition
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Serg Masís
Serg Masís
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Serg Masís

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.
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Mission accomplished

This chapter’s mission was to see whether there was unfair bias in predicting whether a particular defendant would recidivate. We demonstrated that the FPR for African American defendants is 1.87 times higher than for Caucasian defendants. This disparity was confirmed with WIT, indicating that the model in question is much more likely to misclassify the positive class based on race. However, this is a global interpretation method, so it doesn’t answer our question regarding a specific defendant. Incidentally, in Chapter 11, Bias Mitigation and Causal Inference Methods, we will cover other global interpretation methods for unfairness.

To ascertain whether the model was racially biased against the defendant in question, we leveraged anchor and counterfactual explanations – they both output race as a primary feature in their explanations. The anchor did it with relatively high precision and coverage, and Counterfactuals Guided by Prototypes...

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Interpretable Machine Learning with Python - Second Edition
Published in: Oct 2023Publisher: PacktISBN-13: 9781803235424

Author (1)

author image
Serg Masís

Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he's a climate and agronomic data scientist at Syngenta, a leading agribusiness company with a mission to improve global food security. Before that role, he co-founded a start-up, incubated by Harvard Innovation Labs, that combined the power of cloud computing and machine learning with principles in decision-making science to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the often-missing link between data and decision-making—and machine learning interpretation helps bridge this gap robustly.
Read more about Serg Masís