Computing (or computational science) has always been a bit of a confusing term. Is it the science of computing, or is it science (whatsoever) with the help of computing (as in computational physics or mathematics). Of course we have got used to the terminology and use it as deemed fit for the argument.
It becomes however a bit more complicated if we start talking about Digital Science (or Digital Ethics, which I use also myself). Where computing can be seen as a reasonably well defined activity, this cannot be said anymore of Digital. Almost everything that has something to do with information, will relate to the digital world nowadays. So how would we define or describe Digital Science. Internet Science is likely an introspective part of it, as it studies part of the digital world (the Internet). But is seems that it is mostly used for doing science in a digitally enabled way. Like using AI or deep learning for understanding natural or social phenomena.
Now we see emerging the term Citizen Science, and in the white paper of the Socientize project (http://www.socientize.eu/ ) this is described as:
“This new term refers to the ICT-enabled radical transformation of science and innovation within a culture of openness and sharing. Digital Science is more open, global, collaborative, creative and closer to society. One of its basis are the e-infrastructures, services and tools for data and computing intensive research in virtual and collaborative environments. Within the Digital Science in Horizon 2020 Concept Paper, Citizen Science is recognised as trend in the research cycle. Horizon 2020 aims to mainstream Digital Science and Citizen Science will be promoted as part of its objectives.”
It is about ICT enabled science and innovation, but maybe only about the part that concerns radical transformation within a specific culture of science and innovation. There is likely to be more to this description to justify the adjective “Citizen”. And it appears in the white book that indeed the citizen is a subject of, but also active in this kind of science, by engaging in the definition, the data provision and the solution of scientific questions.
DEF does strongly believe that breakthrough solutions for the problems that have arisen in society as a consequence of the digitisation have to come from multi-disciplinary collaboration of experts from SSH, law, economy, technology, life sciences, policy and more. And where possible the engaged citizen should play a role, but without idolizing the “wisdom of the crowd”.
Socientize gives some aspects that ere relevant to Citizen Science, including:
- How to involve citizens in a meaningful way in scientific work, other than as a subject of which data is collected.
- Can we educate citizens well enough to enable them to use the huge citizen data sets to their benefit.
- Should citizens benefit directly from providing their data sets for S&T&I, hence not only for open science, but also for industrial innovation, that creates huge profits from it, without rewarding them, or even to be used against them for profiling, surveillance etc.
- Can we engage citizens in the formulation and resolution of societal problems.
- Do we understand how we can do serious science with large data sets within their context, and do we understand how important context is to achieve statistically meaningful conclusions?
These are only a few important problems. We could add issues related to Ethics of Information, to Human Rights, politics, security, ….
Basically we likely end up with as subject the whole of society and how it evolves as a complex system that can be approximated and modelled locally (to great benefit of citizens), but never tamed as a whole. However big our set of initial data is (see Prigogine, The end of Certainty).
We have to deal with human behaviour, irrationality and interests. These are all context and time-dependent. And people cannot be engaged in too many problems at the same time.
If we look at the first and fourth bullet above, the example of Open Source comes to my mind. It came up strongly at the end of the second millennium and was about engaging people who make and use software in its development. In the early years of the third millennium less than 10% of the people involved in it made more than 90% of the software. Most people were and are simply users, who never actually program or maybe sometimes make small additions for their own personal benefit. Can we really expect to engage a significant percentage of citizens in Citizen Science? In my opinion it will always be changing communities (small or large) who drive specific changes in a society. And then we even forget that still a significant part of citizens have no access to the digital world or use it only for Facebook chats within very small groups of family and friends.
The third bullet has been discussed in much detail by Jared Lanier and more recently by Luciano Floridi in relation to his book on the Ethics of Information. The question should not be if citizens should benefit from their data – the answer is an outright YES, but how this can be organised in a scalable way in society.
On the second and last bullet one might look at the paper of Kate Crawford e.a.  which gives an excellent overview of the problematic of Big Data.
There are of course many more things to say about the initiative which goes under the flag “Citizen Science”. The authors of the white book very well realise that and for that reason made it a white book.
Summarizing I would say: try to narrow the concept down to more actionable issues (formulating the right question will give the solution) and be aware of the fact that a society of citizens cannot be made, but only influenced.
 Kate Crawford, Kate Miltner, Mary L. Gray, Critiquing Big Data: Politics, Ethics, Epistomology, Int. J of Communication 8 (2014) p1663