A business case for interpretability
This section describes several practical business benefits of machine learning interpretability, such as better decisions, as well as being more trusted, ethical, and profitable.
Better decisions
Typically, machine learning models are trained and then evaluated against the desired metrics. If they pass quality control against a hold-out dataset, they are deployed. However, once tested in the real world, things can get wild, as in the following hypothetical scenarios:
- A high-frequency trading algorithm could single-handedly crash the stock market.
- Hundreds of smart home devices might inexplicably burst into unprompted laughter, terrifying their users.
- License-plate recognition systems could incorrectly read a new kind of license plate and fine the wrong drivers.
- A racially biased surveillance system could incorrectly detect an intruder, and because of this guards shoot an innocent office worker.
- A self...
