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Interesting article, but I think this sentence was unfair to other AI scholars, who also want AI to help society. "While many scholars in the developing field of machine learning were focused on improving algorithms, Rudin instead wanted to use AI’s power to help society."
Is this a respected prize? I never heard of it before. That said, no doubt that explainable ML/AI is important.
It's a very new prize (this is only the second winner), so it's still too early to tell. But is backed by a reasonably respectable organization.
Any link to her actual work?
"The machine learning model is a two-layer additive risk model, which resembles a two-layer neural network, but is decomposable into subscales. In this model, each node in the first (hidden) layer represents a meaningful subscale model, and all of the nonlinearities are transparent. Our online visualization tool allows exploration of this model, showing precisely how it came to its conclusion. We provide three types of explanations that are simpler than, but consistent with, the global model: case-based reasoning explanations that use neighboring past cases, a set of features that were the most important for the model’s prediction, and summary-explanations that provide a customized sparse explanation for any particular lending decision made by the model."
I was curious about the customized sparse explanation. It looks like there is an illustrative example from later in the paper:
"For all 700 (7.1%) people where:
• ExternalRiskEstimate ≤ 63 , and
• NetFractionRevolvingBurden ≥ 73,
the global model predicts a high risk of default."
"A rule returned by OptConsistentRule is globally-consistent, in the sense that there exists no previous case that satisfies the conditions in the rule but is predicted differently, by the global model, from what is stated in the rule. In contrast, explanations (from other methods) that are not consistent may hold for one customer but not for another, which could eventually jeopardize trust (e.g., “That other person also satisfied the rule but he wasn’t denied a loan, like I was!”)"
You can see the online visualization tool her team built here: http://dukedatasciencefico.cs.duke.edu/models/
In retrospect, it's not all that surprising to me that a model such as this is able to outperform a black box like a neural network. For example, one of the things this model does which black box models don't do is enforce "monotonicity constraints" which ensure that as risk factors increase, the estimated risk should also increase. It makes sense that this would be a useful inductive bias which improves generalization performance -- if a black box model found that an increase in risk factors decreased estimated risk, it seems likely that this would be a result of overfitting (or multicollinearity gone haywire).
Of course another reason to expect simple/interpretable models to generalize better is Occam's Razor.
My big question about this sort of approach would be whether it's able to extend to the sort of unstructured data problems that deep learning has done really well on. It looks like some of her recent papers on Google Scholar address this: https://scholar.google.com/citations?hl=en&user=mezKJyoAAAAJ... (specifically thinking of the BacHMMachine paper and the Interpretable Mammographic Image Classification paper). Maybe someone else can summarize them.
Is this a respected prize? I never heard of it before. That said, no doubt that explainable ML/AI is important.
Is $1 Million respectable?
If I awarded $1 Million to a random person every year, receiving that award wouldn't make that person more accomplished, and the award wouldn't be mentioned outside the local newspaper. On the other hand an award that gives no money but consistently awards the best researcher in the field can be very noteworthy.
What the $1 million does accomplish is make people pay attention, so everyone will much more quickly reach a verdict whether this is a price worth paying attention to. But two years is a bit quick for that verdict.
Any link to her actual work?
Most engineers here get like £60k salary (£3600 a month after PAYE tax), while companies they work for make billions out of their work. Not only that, but they also don't contribute back into the local communities, because they use aggressive tax avoidance strategies. Corporations need to start sharing their profits with the workers and pay taxes otherwise it will eventually spark another revolution.
Interesting article, but I think this sentence was unfair to other AI scholars, who also want AI to help society. "While many scholars in the developing field of machine learning were focused on improving algorithms, Rudin instead wanted to use AI’s power to help society."
I wish one day I'll read that Computer Scientist _earns_ $1 Million. Most engineers here get like £60k salary (£3600 a month after PAYE tax), while companies they work for make billions out of their work. Not only that, but they also don't contribute back into the local communities, because they use aggressive tax avoidance strategies. Corporations need to start sharing their profits with the workers and pay taxes ot…
Congratulations! I think Interpretable AI mentioned in the article is what is commonly called explainable AI.
"Rather than trying to create models that are inherently interpretable, there has been a recent explosion of work on “Explainable ML,” where a second (posthoc) model is created to explain the first black box model. This is problematic. Explanations are often not reliable, and can be misleading, as we discuss below. If we instead use models that are inherently interpretable, they provide their own explanations, which are faithful to what the model actually computes"
Any link to her actual work?