Category Archives: Interpretability

Properties of Interpretability

In my last two posts, I wrote about model interpretability, with the goal of trying to understanding what it means and how to measure it. In the first post, I described the disconnect between our mental models and algorithmic models, … Continue reading

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Goals of Interpretability

In my last post, I looked at the gap that arises when we delegate parts of our thought processes to algorithmic models, rather than incorporating the rules they identify directly¬†into our mental models, like we do with traditional statistics. I … Continue reading

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Interacting with ML Models

The main difference between data analysis today, compared with a decade or two ago, is the way that we interact with it. Previously, the role of statistics was primarily to extend our mental models by discovering new correlations and causal … Continue reading

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