The Internet of Us (17 page)

Actually, suspicion of the foundationalist picture of the structure of justification is hardly new. The logical positivist Otto Neurath—a member of the famous Vienna Circle gathering
of intellectuals in the early twentieth century—famously suggested another metaphor. He likened justifying our beliefs to rebuilding a raft at sea. If we are to work on one of the planks, we must stand on another. If we later need to repair that second plank, then we must go back to standing on the first. We can't repair all the planks on a boat at sea at the same time. In other words, when we support our beliefs about one kind of thing, we take other beliefs for granted as justified. But we might later throw those into question, and take the first ones for granted. There is no point outside of the raft—outside our framework of beliefs—on which to stand.

A slightly more updated, but similar metaphor might be the “wiki.” A wiki is a platform by which numerous people can participate in shaping a document or webpage. There is no single editor with a single “foundational” vision of how the work should turn out: changes are often made piecemeal, dropped in, replaced, reedited and so on. No single bit of information is immune to change. Or we might think of a fabric: Descartes understood knowledge to be secured by the strength of its foundations, but the fabric metaphor sees it being secured by the strength of its connections. What we know—if we are lucky enough to know—is woven together and constructed from many interlocking strands. Each of the strands supports the rest—some directly, some more remotely. In most weaves, no single thread or set of threads supports any of the others. The support they provide is, we might say, holistic, not linear. The same with spiderwebs. Or World Wide Webs.

The point of all these metaphors is the same. Webs, fabrics, interlocking planks of a raft and wikis are all networks, but they
are not networks with foundational nodes; the nodes are where the individual lines and threads cross. And that, of course, is the point. Our beliefs are nodes in a network, supported by the overall
coherence
of the fabric of beliefs to which they belong.

The “coherentist” picture of reasons does seem like a better description of how we justify our beliefs to one another in the Internet age. Nowadays, when we want to know whether something is true, we look it up on the Web. Practically speaking, that means checking to see how the relevant proposition hangs together with other things we think we know. Suppose I wanted to know the average size of sea turtles. I google it and find several pages that give me an answer. I pick Wikipedia. I then want to check whether Wikipedia is an accurate source of information on sea turtles. So I google that—and find that Wikipedia has an extensive page on whether Wikipedia is reliable (which is, in fact, the case—check it out). I may indeed find that my original belief about the average size of a sea turtle is justified; I find that it is confirmed by a page whose reliability is also confirmed. The whole pattern or structure of reasons here takes the form of an interlocking network.

As we saw in the first part of this book, however, knowledge comes in more than one form. That's crucial to remember right here, for the simple reason that only using Wikipedia to check on Wikipedia is circular. I've never really left the network of information I was consulting. In many cases, that is fine. If the circle of reasons is wide and big enough, we may not need to worry. But generally speaking, being trapped in a circle of reasons is—or should be—a disquieting fact. For it leaves open the possibility that our networks of reasons are just massive and mutually reinforcing
fantasies. If our networks of reasons are really going to be justified, if they are really going to get us knowledge, then at some point they need to be anchored to something else, something beyond themselves. That is why it is a mistake—and I think here Weinberger and I might agree—to think that facts, and justifying our beliefs in light of them, are no longer important to the pursuit of knowledge. Giving up on the Cartesian dream of certain, immutable foundations doesn't mean that we should give up on anchoring our beliefs altogether.

How then
are
they anchored? In two ways. First, by the objective world itself—by what is true and what isn't. That's why, as I urged earlier, we don't want to give up on the idea of truth. The second is that reason-giving isn't all there is to knowing. We can also know by being receptive to the facts outside of ourselves, by having what the contemporary thinker Ernest Sosa calls “animal” knowledge or Descartes before him called
cognitio
.
¹⁵
That's a good thing to remember in this context. My network of reasons isn't just floating at sea. Some of the beliefs for which I have reasons are also ones that I know receptively, by responding to the environment in which I live with the senses I have. Others I may know receptively without being able to defend them with reasons.

Humans can get this kind of anchoring knowledge by getting up off the couch and plunging into the whirlpool of actual experience. It is still the best way, in my view—although not the only way—for us digital humans. To escape your circle of justification, do what you do with any circle: step outside its borders and breathe in the environment on the outside.

Of course for our receptive beliefs to be actually anchored to
reality, reality must cooperate. And sadly, it often does not. That is why we are always forced back to look for reasons, to standards of reasonableness. We need assurance that the anchor is truly set. That gets us back to our network of reasons; we are back in the circle. As the philosopher Duncan Pritchard has noted, it is probably our lot as knowers to always be in some state of angst about our knowledge.
¹⁶
There is no getting around the fact that in order to know receptively, we have to be lucky; we either track the facts around us or we don't and are fooled again. The anchor sets or it doesn't.

I've argued in this chapter that the zeitgeist takes us to be networked knowledge machines. That's one of the lessons to draw from reflecting on the neuromedia thought experiment. As I've pointed out, knowledge and the process of justification is growing more networked in several ways: in its structure, in its source and, most radically, in the fact that our own cognitive capacities themselves are networked. In and of itself, this increasingly networked nature of knowledge isn't good or bad. It is just what is happening. What
can
be good or bad is how we react to this fact. As I've been urging, what we
don't
want to do is assume that because knowledge is networked, the nodes in the network—the individual knowers—no longer matter.

7

Who Gets to Know: The Political Economy of Knowledge

Knowledge Democratized?

The Internet of Things and the networked knower are changing not only
how
we know, they are changing the
politics of knowledge.
And like all politics, the politics of knowledge is about power. In this case, it is the power over who gets to count as a knower and what gets to count as known. As Larry Sanger, philosopher and cofounder of Wikipedia, says, this is an awesome sort of power, because “it can shape legislative agendas, steer the passions of crowds, educate whole generations, direct reading habits and tar as radical or nutty whole groups of people that otherwise might seem perfectly normal.”
¹

For much of Western history, it was the Church that determined what passed for knowledge. The means for exercising this power largely consisted in its ability to control who could read and what was written down—the Church both ran the
universities and controlled the copying (by hand) of texts. Of course, after the print revolution, that began to change. The printing press allowed more people the opportunity to not only write down but mass-produce and distribute their own thoughts. Thus, what counted as knowledge became more diffuse, but also more accessible. Before long, however, power began to shift toward those who controlled the presses and means of distribution—and state imposition of copyright laws and censorship quickly became more prevalent and important. Since the eighteenth century, contemporary liberal societies have slowly (and not without much backsliding) made efforts to curtail state censorship and to allow ideas to spread more freely. Of course they too have had their own gatekeepers, even if their gates were more permeable: libraries, universities, publishers, the media. Yet as anyone who has been paying attention to these trends knows, those gates too have been coming down.

The Internet, it is often said, is democratizing knowledge. This is perhaps the single most heralded upside of the changes in informational technology we've been experiencing for the last two decades. But what does it mean to “democratize” knowledge—and how might current technologies contribute to that process?

First, and most obviously, the Internet, like the printing press before it, has made bodies of
knowledge more widely available
. The possibility of mass-produced books lowered the price at which knowledge could be bought and sold. As such, it brought such knowledge—and the possibility of literacy—to millions of people who had previously lacked access to it. Web 2.0 has greatly
expanded this process while also changing both the sheer amount of different kinds of information available and the speed at which that information can be accessed.

A good example concerns this very topic. Try googling “How many people have access to the Internet,” and sources such as Wikipedia and the International Telecommunications Union in Geneva will tell you that while roughly 94 percent of Swedes have Internet access, and 84 percent of Americans, only 2.1 percent of the population of Chad does. Nonetheless, the very availability of these statistics is a great example of the sort of information that just a few years ago you'd have had to go to a large research university to find or rely on journalists to report. While billions of people continue to have no access to it, millions have immediate access to the sort of information they wouldn't have had just a decade ago. In short: while it is far from ubiquitous, “more information to more people” is one obvious way that the Internet is making knowledge—or its acquisition—“more democratic.”

The Internet is also democratizing knowledge by making its
production more inclusive
. One common example here is open source software like Mozilla's Firefox Web browser. When security vulnerabilities or bugs arise in Firefox software, a diverse and widespread community of volunteers works on fixes and plug-ins. Open source software operates similarly to an online co-op. It is software by the people, for the people.

Epistemic inclusivity is also a by-product of the growing number of open access research sharing sites such as Academia.edu. Founded in 2008, Academia.edu allows its millions of users (I'm one) a platform upon which to share and comment on one
another's research. It allows researchers to pass their work
directly
to those who might be interested in it, or benefit from it.

Inclusivity of a different sort can come about by what
Wired
's Jeff Howe dubbed “crowdsourcing” in 2006. Crowdsourcing is not simply any activity that uses the World Wide Web as a platform for people to network about problems—like Intrade or rankings on Amazon. As computer scientist Daren Brabham defines it, crowdsourcing is an online problem-solving and production model that “leverages the collective intelligence of online communities to serve specific organizational goals.”
²
In other words, it is the top-down organized use of the Internet hive-mind. An organization throws out a problem, and those who want (or those granted access to the relevant site or network) contribute solutions, and see what sticks.

The popular incentive-based innovation platform InnoCentive is often cited as an example of the inclusivity of crowdsourcing. (InnoCentive is ancient in Web 2.0 terms: it was founded in 2002.) Here's how it works: nonprofits and businesses post prize competitions for solutions to challenges. These can run the gamut—from retail product positioning to early detection mechanisms for inflammatory bowel disease. The prizes themselves vary in size, with some topping nearly a million dollars but many being significantly less. InnoCentive is only one example of how crowdsourcing can work, of course. Other famous examples include Amazon's Mechanical Turk, which allows companies (and scientific researchers) to outsource specific tasks to a huge network of “Turkers” to perform tasks that humans are still better at than computers, such as image identification and translation. Still another is Threadless, an organization that assigns a
crowd of T-shirt designers the job of selecting (and creating) new T-shirt designs.

Challenge-specific prizes, like those used by InnoCentive, have been useful sources of innovation in scientific research for centuries. The British Crown spurred a huge leap forward in marine navigation in the seventeenth century, for example, by offering a prize for a device that could calculate a ship's longitude—resulting in the invention of the marine chronometer. Competitions like this work partly because they provide an incentive for “fresh eyes” on the problem. Indeed, researchers Lars Bo Jeppesen and Karim Lakhani, in their 2010 study of InnoCentive, suggested that there is an inverse relationship between a solver's likelihood of solving a problem and his or her degree of expertise in the field in question.
³
As Brabham writes, this means that, for example, “a biologist may fare better than a chemist would at solving a chemical engineering problem.”
⁴
The same study also found that women significantly outperformed men as problem solvers on the site—despite, and possibly because of, the fact that they are often on the edges of the “scientific establishment.” That is inclusivity of a very obvious sort.