[pragmacoders]

I'm curious what a solution like "All Machine Learning algorithms, and the data that taught them, must be open-source and easily accessible by the public" would look like.

It'd allow for public scrutiny at the algorithms that run our lives. It'd essentially mandate thorough vetting of how anonymous and/or biased the data being fed into these algorithms is.

Which would be interesting

[moultano]

You really don't want that. https://en.m.wikipedia.org/wiki/AOL_search_data_leak

[pragmacoders]

Well the idea would be that companies would be forced to actually figure out how to properly clean data or forced not to use it out of public pressure

[Mirioron]

It's probably not possible to clean data like that. Just a few slices of a person's life are enough to uniquely identify them. So all you're asking then is effectively a ban on machine learning.

[matt4077]

Apple and others have poured a lot of resources into a concept called “differential privacy” that can mathematically guarantee privacy for data in a machine learning context.

It’s rather well known, so you should probably read up on it before espousing strongly worded opinion weakly connected to the current state of the art.

[moultano]

I don't think anyone has managed to train differentially private models with acceptable performance, and releasing raw data in differentially private way seems impossible to me.

[pragmacoders]

What happened to innovation? Innovation on privacy isn't possible?

The only way we can assure that cryptographic algorithms are in fact secure is to subject them to public scrutiny over many years.

Perhaps the only way to ensure that machine learning algorithms are not racist, harmful or biased is to have access to both the algorithm itself, the trained model, and the data that created it.

Algorithms can be biased. Data can be biased. Trained models can be biased.

We can't trust the government or companies to prove that a cryptographic algorithm is secure and we can't trust the government or companies to prove that an algorithm is unbiased and unharmful.

[Mirioron]

It would either kill machine learning or it would give everyone's personal data out to everyone else.