zlacker

>>rand0m+(OP)
> Fair use is a doctrine in the law of the United States that allows limited use of copyrighted material without requiring permission from the rights holders. It provides for the legal, non-licensed citation or incorporation of copyrighted material in another author's work under a four-factor balancing test:

> 1) The purpose and character of the use, including whether such use is of a commercial nature or is for nonprofit educational purposes

> 2) The nature of the copyrighted work

> 3) The amount and substantiality of the portion used in relation to the copyrighted work as a whole

> 4) The effect of the use upon the potential market for or value of the copyrighted work

[emphasis from TFA]

HN always talks about derivative work and transformativeness, but never about these. The fourth one especially seems clear in its implications for models.

Regardless, it makes it seem much less clear cut than people here often say.

>>6gvONx+qs
Unpopular opinion time:

A ML model is clearly a derivative work of its input.

Here's what I think would be fair:

Anyone who holds copyright in something used as part of a training corpus is owed a proportional share of the cash flow resulting from use of the resulting models. (Cash flow, not profits, because it's too easy to use accounting tricks to make profits disappear).

In the case of intermediaries (e.g., social media like reddit & twitter) those intermediaries could take a cut before passing it on to the original authors.

Obviously hellishly difficult to administer so it's unlikely to happen but I don't see a better answer.

>>beware+Av
> A ML model is clearly a derivative work of its input.

Do you mean this in a copying sense or a mathematical sense?

What if it's only storing 1 byte per input document?