Mathwashing

https://www.mathwashing.com/

by WillDaSilva • 6 years ago

5 4 6 years ago

cesarosum2• 6 years ago

A lot of my job involves reviewing other people's models and their implementation including the code and the data to train the models. If the author or team has put together a mathematical description, that's usually a good sign that they've done things with a degree of consideration and capability. However, I do have to remind myself not to fall for the mistake of conflating mathematical proficiency with correspondence to a "correct" model of the real world. The circuit breaker for me is to test my thinking and assessment with someone else. We're all suckers for something.

[EDIT] Grammar.

dfischer2• 6 years ago

Under what principles and criteria do you standardize the review? What's the checklist?

If you are able to provide. Thanks.

cesarosum2• 6 years ago

The company I work for builds enterprise software, so the key question that has to be addressed is "does this solve the business problem?". Most of what I review are implementations of some form of standard supervised learning model that has been tweaked for performance at scale, but I also see unsupervised learning, causal modeling, optimisation and some statistical analyses. As such, the criteria changes depending on the particular problem and I'm afraid I can't give detailed examples. However, in the case of an implementation of a standard supervised learning model, some common criteria are:

1. Is the statement of the business problem well-defined with a clear outcome that can be measured? Does it change for different clients?

2. Where is the data sourced from? Who or what created the labels? Any known discrepancies or errors (systematic or random)? Is it standard across clients?

3. Is the model appropriate for a) the problem to be solved b) the data available for training and validation?

4. Is the model performant with regards to the specified performance metrics? What's the cost of a false positive/negative?

5. How often will the model be re-trained? What's the justification?

6. Has the model been validated on data that is out of sample (usually includes out of time and out of population)?

7. Have biases in the data been identified? What has been done to address this?

8. What assumptions have been made about the data? Have these been tested?

9. What second-order effects do you anticipate will result from putting this model into production? Will these affect the data being collected and used to re-train the model?

dfischer2• 6 years ago

Happy to see this being discussed. It's quite important and there's been some resistance in having these conversations.

Mathwashing

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