Dr. Rachel Thomas writes,
When we think about AI, we need to think about complicated real-world systems [because] decision-making happens within complicated real-world systems.
For example, bail/bond algorithms live in this world:
for public defenders to meet with defendants at Rikers Island, where many pre-trial detainees in NYC who can’t afford bail are held, involves a bus ride that is two hours each way and they then only get 30 minutes to see the defendant, assuming the guards are on time (which is not always the case)
Designed or not, this system leads innocents to plead guilty so they can leave jail faster than if they waited for a trial. I hope that was not the point.
Happily I don’t work bail/bond algorithms, but one decision tree is much like another. ”We do things right” means I need to ask more about decision context. We know decision theory - our customers don’t. Decisions should weigh the costs of false positives versus false negatives. It’s tempting to hand them the maximized ROC curve and make threshold choice Someone Else’s Problem. But Someone Else often accepts the default.
False positives abound in cyber-security. The lesser evil is being ignored like some nervous “check engine” light. The greater is being too easily believed. We can detect anomalies - but usually the customer has to investigate.
We can help by providing context. Does the system report confidence? Does it simply say “I don’t know?” when appropriate? Do we know the relative costs of misses and false alarms? Can the customer adjust those for the situation?