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Comment by motohagiography | Hacker News Reader

I tell people, the main use case for ML is to obfuscate and launder decision authority and accountability away from people and up into abstract organizations where nobody is accountable. If there is ever a question as to why the police made a decision, they can say it was their prediction model, which is so complex nobody could reasonably expect to understand it or be responsible for its outcomes.

It's as though artificial intelligence itself were a cryptological problem where it's only real when it becomes sufficiently complex that information about who is accountable for it is destroyed.

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I think one of the most important things we can do is to establish liability for prediction models to the people using the model.

At the end of the day if you falsely arrest someone, fire them on bad grounds, etc then you're the responsible party. The excuse "Well but someone told me to!" doesn't work any better when you're blaming a machine instead of a person - it's still bunk.

If the model is broken, well then that's on you. A vendor sold you a magical model saying it was perfect and you got in trouble anyways? Well, you can go on and try to sue the vendor and hash that out - after you pay for your mistake.

It's critically important we don't allow models to become get out of jail free cards.

Other people who should be liable, at least as much as those using the model:

1. sellers/providers of the model, if they lied about the efficiency

2. buyers/deciders, who not necessarily the same as those using the model (usually their bosses)

You can still go after a seller for making false claims, but you are liable for your actions - full stop. Unless the seller forced you to fire someone or arrest someone based on the model, you executed the action.

Someone who writes your legal contract isn't liable if you badly enforce it.

Discussions around responsibly applying probabilistic models to decision making at a societal level are really important to have.

Myth-making about ML as a shadowy conspiracy—instead of just another tool for engineering, like databases—is counter-productive.

The "main use case" for ML is the literal one, "designing models that can learn to predict high probability outcomes in situations where a deterministic model cannot be applied." It is another technology in an engineer's toolkit. Look at your smartphone, and you will see that the majority of apps you use rely on machine learning for ETA prediction, content recommendation, speech-to-text, image processing, translation, spam filtering, etc.

It's worked ridiculously well for Wall Street.

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