FAIR is not fair enough

The FAIR data principles, defined as "a set of guiding principles to make data Findable, Accessible, Interoperable, and Re-usable," came out of a meeting in Jan 2014 that "brought together 25 high level participants representing leading research infrastructures and policy institutes, publishers, semantic web specialists, innovators, computer scientists and experimental (e)Scientists."

The idea of FAIR seems to be catching on, and potentially being applied to other types of objects, such as software.  For example, a recent paper, "Four simple recommendations to encourage best practices in research software" (of which I am one of many co-authors), says:

"While the FAIR principles were originally designed for data, they are sufficiently general that their high level concepts can be applied to any digital object including software. Though not all the recommendations from the FAIR data principles directly apply to software, there is good alignment between the OSS recommendations [the software recommendations in the paper] and the FAIR data principles"

However, it is clear to me that settling for FAIR is not really fair enough.

First, note that FAIR doesn't actually require the objects (e.g., data, software) to be openly available.  The FAIR principles narrowly define accessibility as the metadata protocol being open and that the metadata themselves are accessible, not the data (or in our case, software.)  As I might have once heard on a playground, "not sharing is not fair."

Some might argue that the spirit of the FAIR principles is more generous than the letter, and this may be true.  Many readers of the FAIR principles may take them to mean that objects should be available.  But this is not what the principles actually say, and I think this is important.

Second, FAIR doesn't include the idea of credit. In academia, credit is important, as it's currently a key factor in hiring and promotion. However, there are philosophical differences regarding credit. For example, the open source software movement is generally not concerned with credit, though efforts like Depsy and Libraries.io are trying to inject it. This idea of creating works without being concerned about credit has also been taken up by the Maker movement.  Influenced by both of these, Cory Doctorow in a recent book, Walkaway, suggests a future where the idea of credit is strongly discouraged, with a main character stating, "We're making a world where greed is a perversion."

In general, our global society uses credit as an incentive to encourage production of objects and ideas, where credit is financial, intellectual, or academic.  A commons model, which works in small groups, does not use credit in this way, but rather, encourages production that benefits the group as a whole.  I personally do not believe that this model scales to larger groups, and thus, I believe credit is essential. In addition to the personal benefit to credit, there is a group benefit: recognizing and using the expertise of individuals is how large communities function effectively.

An example of another group that is adding to FAIR is the FORCE11 Scholarly Commons working group, a group that is "exploring what is required for a scholarly communication ecosystem designed for 21st century scholarship," where we seem to be moving towards "open, FAIR, and citable" as a goal for objects in the scholarly commons, with the idea that these three concepts can leverage each other. For example, see the description of course AM2: Scholarship in the 21st Century in this summer's FORCE11 Scholarly Communications Institute. However, this is by no means a settled issue within this working group.

In summary, I think that products that are not open are unlikely to fully benefit the research community, and while I am somewhat sympathetic to the concept of a world where credit is not important, I don't think it's realistic.  Thus, I believe this larger set of attributes, "open, FAIR, and citable," are more fair than just FAIR, and are much more likely to lead to more and better research.

Thanks to Melissa Haendel, Fiona Murphy, and Daniel Paul O’Donnell for useful feedback, though all opinions (and any errors) are mine.