A theme I’ve heard raised in a couple places recently, including Ibarra et al. “Should We Treat Data As Labor?” and the AI Now 2018 Report, is that there is something wrong with how “data”, particularly data “produced” by people on the web, is conceptualized as part of the economy. Creating data, the argument goes, requires labor. And as the product of labor, it should be protected according to the values and practices of labor movements in the past. In particular, the current uses of data in, say, targeted advertising, social media, and search, are exploitative; the idea that consumers ‘pay’ for these services with their data is misleading and ultimately unfair to the consumer. Somehow the value created by the data should be reapportioned back to the user.
This is a sexy and popular argument among a certain subset of intellectuals who care about these things. I believe the core emotional appeal of this proposal is this: It is well known that a few well-known search engine and social media companies, namely Google and Facebook, are rich. If the value added by user data were in part returned to the users, the users, who are compared to Google and Facebook not rich, would get something they otherwise would not get. I.e., the benefits for recognizing the labor involved in creating data is redistribution of surplus to The Rest of Us.
I don’t have a problem personally with that redistributive impulse. However, I don’t think the “data is labor” argument actually makes much sense.
Why not? Well, let’s take the example of a search engine. Here is the transaction between a user and a search engine:
- Alice types a query, “avocado toast recipes”, into the search engine. This submits data to the company computers.
- The company computers use that data to generate a list of results that it deems relevant to that query.
- Alice sees the results, and maybe clicks on one or two of them, if they are good, in the process of navigating to the thing she was looking for in the first place.
- The search engine records that click as well, in order to better calibrate how to respond to others making that query.
We might forget that the search engine is providing Alice a service and isn’t just a ubiquitous part of the infrastructure we should take for granted. The search engine has provided Alice with relevant search results. What this does is (dramatically) reduce Alice’s search costs; had she tried to find the relevant URL by asking her friends, organically surfing the web, or using the library, who knows what she would have found or how long it would take her. But we would assume that Alice is using the search engine because it gets her more relevant results, faster.
It is not clear how Alice could get this thing she wants without going through the motions of typing and clicking and submitting data. These actions all seem like a bare minimum of what is necessary to conduct this kind of transaction. Similarly, when I got to a grocery store and buy vegetables, I have to get out my credit card and swipe it at the machine. This creates data–the data about my credit card transaction. But I would never advocate for recognizing my hidden labor at the credit card machine is necessary to avoid the exploitation of the credit card companies, who then use that information to go about their business. That would be insane.
Indeed, it is a principle of user interface design that the most compelling user interfaces are those that require the least effort from their users. Using search engines is really, really easy because they are designed that way. The fact that oodles of data are collected from a person without that person exerting much effort may be problematic in a lot of ways. But it’s not problematic because it’s laborious for the user; it is designed and compelling precisely because it is labor-saving. The smart home device industry has taken this even further, building voice-activated products for people who would rather not use their hands to input data. That is, if anything, less labor for the user, but more data and more processing on the automated part of the transaction. That the data is work for the company, and less work for the user, indicates that data is not the same thing as user labor.
There is a version of this argument that brings up feminism. Women’s labor, feminists point out, has long been insufficiently recognized and not properly remunerated. For example, domestic labor traditionally performed by women has been taken for granted, and emotional labor (the work of controlling ones emotions on the job), which has often been feminized, has not been taken seriously enough. This is a problem, and the social cause of recognizing women’s labor and rewarding it is, ceteris paribus, a great thing. But, and I know I’m on dicey ground here, so bear with me, this does not mean that everything that women do that they are not paid to do is unrecognized labor in the sense that is relevant for feminist critiques. Case in point, both men and women use credit cards to buy things, and make telephone calls, and drive vehicles through toll booths, and use search engines, and do any number of things that generate “data”, and in most of these cases it is not remunerated directly; but this lack of remuneration isn’t gendered. I would say, perhaps controversially, that the feminist critique does not actually apply to the general case of user generated data much at all! (Though is may apply in specific cases that I haven’t thought of.)
So in conclusion, data isn’t labor, and labor isn’t data. They are different things. We may want a better, more just, political outcome with respect to the distribution of surplus from the technology economy. But trying to get there through an analogy between data and labor is a kind of incoherent way to go about it. We should come up with a better, different way.
So what’s a better alternative? If the revenue streams of search engines are any indication, then it would seem that users “pay” for search engines through being exposed to advertising. So the “resource” that users are giving up in order to use the search engine is attention, or mental time; hence the term, attention economy.
Framing the user cost of search engines in terms of attention does not easily lend itself to an argument for economic reform. Why? Because search engines are already saving people a lot of that attention by making it so easy to look stuff up. Really the transaction looks like:
- Alice pays some attention to Gob (the search engine).
- Gob gives Alice some good search results back in return, and then…
- Gob passes on some of Alice’s attention through to Bob, the advertiser, in return for money.
So Alice gives up attention but gets back search results and the advertisement. Gob gets money. Bob gets attention. The “data” that matters is not the data transmitted from Alice’s computer up to Gob. Rather, the valuable data is the data that Alice receives through her eyes: of this data, the search results are positively valued, the advertisement is negatively valued, but the value of the bundled good is net positive.
If there is something unjust about this economic situation, it has to be due to the way consumer’s attention is being managed by Gob. Interestingly, those who have studied the value of ‘free’ services in attentional terms have chalked up a substantial consumer surplus due to saved attention (Brynjolfsson and Oh, 2012) This appears to be the perspective of management scientists, who tend to be pro-business, and is not a point repeated often by legal scholars, who tend to be more litigious in outlook. For example, legal scholarship has detailed the view of how attention could be abused through digital market manipulation (Calo, 2013).
Ironically for data-as-labor theorists, the search-engine-as-liberator-of-attention argument could be read as the view that what people get from using search engines is more time, or more ability to do other things with their time. In other words, we would use a search engine instead of some other, more laborious discovery mechanism precisely because it would cost us net negative labor. That absolutely throws a wrench in any argument that the users of search engines should be rewarded on dignity of labor grounds. Instead, what’s happened is that search engines are ubiquitous because consumers have undergone a phase transition in their willingness to work to discover things, and now very happily use search engines which, on the whole, seem like a pretty good deal! (The cost of being-advertised-to is small compared to the benefits of the search results.)
If we start seeing search engines as a compelling labor-saving device rather than a exploiter of laborious clickwork, then some of the disregard consumers have for privacy on search engines becomes more understandable. People are willing to give up their data, even if they would rather not, because search engines are saving them so much time. The privacy harms that come as consequence, then, can be seen as externalities to what is essentially a healthy transaction, rather than a perverse matter of a business model that is evil to the bone.
This is, I wager, on the whole a common sense view, one that I’d momentarily forgotten because of my intellectual milieu but now am ashamed to have overlooked. It is, on the whole, far more optimistic than other attempt to characterize the zeitgeist of new technology economy.
Somehow, this rubric for understanding the digital economy appears to have fallen out of fashion. Davenport and Beck (2001) wrote a business book declaring attention to be “the new currency of business”, which if the prior analysis is correct makes more sense than data being the new currency (or oil) of business. The term appears to have originated in an article by Goldhaber (1997). Ironically, the term appears to have had no uptake in the economics literature, despite it being the key to everything! The concept was understood, however, by Herbert Simon, in 1971 (see also Terranova, 2012):
In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.
(A digitized version of this essay, which amazingly appears to be set by a typewriter and then hand-edited (by Simon himself?) can be found here.)
This is where I bottom out–the discover that the line of thought I’ve been on all day starts with Herbert Simon, that the sciences of the artificial are not new, they are just forgotten (because of the glut of other information), and exhaustingly hyped. The attention economy discovered by Simon explains why each year we are surrounded with new theories about how to organize ourselves with technology, when perhaps the wisest perspectives on these topics are ones that will not hype themselves because their authors cannot tweet from the grave.
Arrieta-Ibarra, Imanol, et al. “Should We Treat Data as Labor? Moving beyond” Free”.” AEA Papers and Proceedings. Vol. 108. 2018.
Brynjolfsson, Erik, and JooHee Oh. “The attention economy: measuring the value of free digital services on the Internet.” (2012).
Calo, Ryan. “Digital market manipulation.” Geo. Wash. L. Rev. 82 (2013): 995.
Davenport, Thomas H., and John C. Beck. The attention economy: Understanding the new currency of business. Harvard Business Press, 2001.
Goldhaber, Michael H. “The attention economy and the net.” First Monday 2.4 (1997).
Simon, Herbert A. “Designing organizations for an information-rich world.” (1971): 37-72.
Terranova, Tiziana. “Attention, economy and the brain.” Culture Machine 13 (2012).