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One question that is not clear to me is what should the dataset license to allow / restrict, in the perfect world. For me (just a personal opinion) it would all

by ptero 9y ago

One question that is not clear to me is what should the dataset license to allow / restrict, in the perfect world. For me (just a personal opinion) it would allow free (as in liberty) use, but somehow encourage those who use it to share the benefits (data, software or algorithms) under the same license. Unfortunately, Open Source does not help here -- I do not see how OS can be used with data sets. The main OS leverage with software development is that if you use software X to build software Y, X is usually present in some way, shape or form in your deliverable Y. Not so with training data -- once algorithm development is done you can (and usually do) strip training data out and have a finished product that does not require X to run. Even if one were to require open sourcing derived datasets it is usually easy to segregate the dataset with a tainted (open source) license as you build up your data so the new datasets are not formally "derived" and thus would not need open sourcing. I would love a better way forward on this, or at least a cleaner explanation of options.

nerdponx 9y ago
OS helps tremendously in reproducibility. Without the underlying data, there is no way to audit an analysis. Moreover, an algorithm is only ever "done" in the same way that software is ever "done". New techniques might come along that could enhance the model, or the business requirements might change that necessitate re-tuning the algorithm. The benefits of OS data are the same as the benefits of OS software. The distinction between "Free" and "Open" is the same as well. Edit 1: OS data sets are nothing new. The UCI Machine Learning Repository[1] has been around for years. There is also an entire Open Data Stack Exchange site [2], and an Open Data Subreddit [3]. Edit 2: OS data sets are essential for developing new algorithms because they can be used as benchmarks. Nobody should trust a model that's been developed on a proprietary data set for use on anything other than that one data set. [1]: https://archive.ics.uci.edu/ml/ https://archive.ics.uci.edu/ml/ [2]: https://opendata.stackexchange.com/ https://opendata.stackexchange.com/ [3]: https://reddit.com/r/opendata/ https://reddit.com/r/opendata/
- ptero 9y ago
  Maybe I was not clear -- I am not arguing for proprietary datasets. Validation on publicly available data is a key component in comparisons, assessments, etc. However, there is a whole bestiary of open source licenses that span the spectrum of "use any way you want" to much more restrictive. But they were mostly thought through for software and data is different; what may prevent proprietary abuse in software may not have any teeth for data.

by ptero 9y ago

nerdponx 9y ago

OS helps tremendously in reproducibility. Without the underlying data, there is no way to audit an analysis. Moreover, an algorithm is only ever "done" in the same way that software is ever "done". New techniques might come along that could enhance the model, or the business requirements might change that necessitate re-tuning the algorithm. The benefits of OS data are the same as the benefits of OS software. The distinction between "Free" and "Open" is the same as well. Edit 1: OS data sets are nothing new. The UCI Machine Learning Repository[1] has been around for years. There is also an entire Open Data Stack Exchange site [2], and an Open Data Subreddit [3]. Edit 2: OS data sets are essential for developing new algorithms because they can be used as benchmarks. Nobody should trust a model that's been developed on a proprietary data set for use on anything other than that one data set. [1]: https://archive.ics.uci.edu/ml/ https://archive.ics.uci.edu/ml/ [2]: https://opendata.stackexchange.com/ https://opendata.stackexchange.com/ [3]: https://reddit.com/r/opendata/ https://reddit.com/r/opendata/

ptero 9y ago
Maybe I was not clear -- I am not arguing for proprietary datasets. Validation on publicly available data is a key component in comparisons, assessments, etc. However, there is a whole bestiary of open source licenses that span the spectrum of "use any way you want" to much more restrictive. But they were mostly thought through for software and data is different; what may prevent proprietary abuse in software may not have any teeth for data.