Here, we describe two key developments that characterize the opportunities and challenges we face.

Datafication refers to the development that our everyday activities are traced digitally at unprecedented scale and accuracy for commercialization and exploitation in a data economy. Datafication raises questions about how this situation can or should be managed and what might result out of its pervasiveness. The processes of datafication, their consequences and how we live with these are both social and technical. From the beginning, the question of what data is created depends both on human activities and technical devices.

How this data is used depends on configurations of ownership, markets, state authority, and citizens’ rights as well as the technical affordances for circulation through technical infrastructures and the computational possibilities for analysis. To even describe the processes of datafication demands expertise of the highest level from computer science, law, political science, sociology, and more. To consider if and how society might respond to this new landscape likewise. What are the opportunities to flip data ownership from the big tech companies to the individuals whose data fuels the data economy? Engineering solutions, as developed in the SoLiDe project, may be part of the response, but how can we be sure that people even want let alone will have the capacity to use these solutions? What new challenges might these solutions pose? How would this impact on the underlying business model for the Web?

The digital divide. Web access con-
tinues to rise rapidly but over three bil-
lion people worldwide have no access,
and 1: 8 of the European population
does not use the Web regularly.f We
should avoid normative claims that the
Web is ‘good’ for everyone, we know
now that this is not the case, yet at the
same time this should be a matter of
choice not constraint. Further, beyond
the question of access alone, we see an
increasing divide between those highly
skilled users who are able to derive the
e https://solid.mit.edu/
f https://www.statista.com/topics/3853/inter-
net-usage-in-europe/ref.
greatest benefit and those less skilled
who are less knowledgeable about
privacy risks, less able to protect their
security and may derive less economic
benefit from the opportunities avail-
able online. 17 So long as people are
unaware of the technical mechanisms
and social uses of datafication or the
potential effects of this on their lives
and life chances they will not be able
to make effective choices about how
to use the Web or join the public de-
bate about the future of the Web. Web
Science calls for new approaches to
digital literacy, beyond the use of Web
tools and beyond the extension of cod-
ing skills to schools (important as both
these are) to build understanding of
the Web as a sociotechnical system and
drive toward greater empowerment of
Web citizens. It engages, for example,
through the Web We Want campaign,
#fortheweb, and educational interven-
tions. 11

Both these examples are linked to wider practical, political, and philosophical questions. What are the checks and balances with regard to openness and privacy? What forms of transparency and accountability are appropriate and achievable, to balance individual privacy, fairness across social groups and a viable business model for the future of the Web? How do we engage the public in meaningful dialogue and decision making about the future of the Web?

Next, we investigate another most prominent sociotechnical challenge in more detail that today is most often characterized as a technical challenge alone, whereas it is deeply entrenched into the way that we as individuals or as society interact with each other and with the artifacts we create.

Web and Artificial Intelligence

The Web and its infrastructure has be-
come interwoven not only with docu-
ments, but also with data, services,
things—and artificial intelligences.
Initially, the Web was a field of ap-
plication for artificial intelligence.
Knowledge-based systems and ma-
chine learning were used to provide in-
telligent access to information on the
Web, to enhance search, to facilitate
browsing or to negotiate in electron-
ics market. In hindsight, this may be
considered to have been a very useful,
Yet since the end of its first decade,
there was a vision to build a Web that
was intelligent in itself, that included
agents that would assist its users. 6 As
this objective was beyond reach then,
the Semantic Web community in-
creasingly refocused on what became
a proverb that data with a little seman-
tics goes a long way. When researchers
started to properly understand and use
the social motivation of Web develop-
ers and Web content managers, some
European researchers developed what
now has become the two most popular
Semantic Web applications, Wikida-
ta27 and Schema.org.g At the same time
Web Science was coined as a field that
would address the systematic under-
standing of these socio-technical in-
teractions between Web and humans. 7

At the end of the second decade of the Web, artificial intelligence took several major turns. Big data, which frequently came from the Web directly or from crowdsourcing on the Web, became the foundation for human-like performance on some tasks such as image annotations. 19 At the same time chatbots and virtual assistants have been developed and are now widely found on our PCs, smartphones, and in our homes. The latest developments let these virtual assistants acquire their knowledge from the Web, from archived dialogues, 12 or from live interaction.

Microsoft researchers were pushing the edge and put their AI chatbot “Tay” online to interact with and learn from human encounters. Humans quickly taught it to go ˛<<from “humans are super cool” to full nazi in <24hrs>>.h While there was a wide discussion that the technology was inadequate, there seemed to have been little understanding that it was the social context and the social processes that determined the fate of Tay. While in the initial Semantic Web, the lack of such understanding led to a simple, but not very problematic non-adoption, in the case of Tay being an active agent the lack of g Schema.org was an agreement of several search companies modeled after the preceding Yahoo! SearchMonkey system. 21

h https://www.theverge.com/2016/3/24/ 11297050/tay-microsoft-chatbot-racist