I'll defer to others on the finer points of how downstream or intermediate ML products fit into the licensing picture, but this did catch my eye:
> If you need an example: Take a translator for geographic names trained > using OSM data. This translator in practical use will spit out names > or name components identical to those from the OSM database (if it does > not it'd be pretty useless). These names - in sufficient volume - > evidently form a derivative database IMO - even if they are not the > result of a literal copy but result from 'knowledge' encoded in a > neural network. I have sometimes sene similar arguments about intellectual property brought up in engineering-focused conversations, which propose elaborate technical mechanisms by which data might be transformed, then recreated, and in the process its intellectual property rights somehow purged. There are already several community guidelines explaining why this isn't acceptable. Beyond that, my own sense is that this is at odds with how the legal system approaches these questions (and confusion about what "transformative use" means). If a party has custody of proprietary data, feeds it through a black box that a judge and jury don't really understand, and the original data comes out the other side--well, you can see why it's a hard argument to win. I think the risk of recreating OSM data via ML trickery is pretty low: it's an elaborate approach that offer dubious legal advantages. If the question is whether OSM could claim rights over a fictional but plausible map-like output from an OSM-trained ML model, I'd say the question is more open. But I suspect this is a less worrisome scenario for most. On Wed, Apr 10, 2019 at 10:12 AM Christoph Hormann <chris_horm...@gmx.de> wrote: > On Wednesday 10 April 2019, althio wrote: > > > > You may have skipped parts of my message, so excuse me if I repeat a > > few lines. You quoted only two sentences and I slightly wonder if you > > genuinely read the whole. > > I am sorry if i left the impression that i was specifically criticizing > your ideas - i was more referring to the general course of the > discussion towards a rather mechanical exegesis of the ODbL based on a > simplistic view of how algorithms work as a mechanical process > converting well defined input data into well defined output data. > > > [...] > > I don't think my original message can be read as "sweepingly declare > > any output of algorithms as having no copyright connection". > > I did not mean to imply that - but since your line of reasoning only > covers this case it is to be expected that people assume this is the > only relevant case. > > > [...] > > > > My final two cents: > > Take the Geocoding guideline, replace "Geocoding" by "Machine > > Learning" and this is, in my humble opinion, an acceptable first > > draft for discussion. > > But as far as i understand you, you up-front want to declare > the "database" behind the Machine Learning, i.e. the adaptive part of > the algorithms that gets modified through training, to be a produced > work and therefore not subject to share-alike. > > If not i don't see the practical usefulness in applying the geocoding > guideline to this in analogy because while for geocoding the individual > result is a frequent practical use case Machine Learning and similar > algorithms are mostly used to produce bulk results which are usually > substantial in terms of database law. > > As far as the Horizontal Layers guideline and the concept of produced > works in general is concerned - the only consistent view of these > concepts is IMO to consider them to be limited exclusively to cases > when you are talking about things produced for and used only for direct > human consumption. > > -- > Christoph Hormann > http://www.imagico.de/ > > _______________________________________________ > legal-talk mailing list > legal-talk@openstreetmap.org > https://lists.openstreetmap.org/listinfo/legal-talk >
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