In light of the Radical Markets book chapter entitled Data as Labor, its co-author Glen Weyl invited me to write this post for the RadicalxChange blog, published 9 Feb 2019.
The RadicalxChange mission dedicates the community to “using dramatically expanded competitive, free and open market mechanisms to reduce inequality, build widely-shared prosperity, heal global political divides and build a richer and more cooperative social life.”
Our values are aligned and our purpose very similar, but I will describe here some important considerations in designing for personal data that may not yet have received attention by the RadicalXChange community.
Co-operating is essential
It is, I think, critical to outline the emphasis I adopt here in framing the problem and the opportunity. Whereas the RadicalxChange mission aims to have competition lead to greater co-operation, I treat competition and co-operation here as equal and concurrent. Here are two quotes by way of explanation.
The brilliant and indomitable biologist Lynn Margulis discovered that:
The view of evolution as a chronic bloody competition among individuals and species, a popular distortion of Darwin’s notion of “survival of the fittest,” dissolves before a new view of continual cooperation, strong interaction, and mutual dependence among life forms. Life did not take over the globe by combat, but by networking.
And the progenitor of the invisible hand himself, Adam Smith in the Wealth of Nations, actually considered his Theory of Moral Sentiments to be the superior work. It is, as Gintis et al write, the perfect counterbalance.
Effective policies are those that support socially valued outcomes not only by harnessing selfish motives [Wealth of Nations / competition] to socially valued ends, but also by evoking, cultivating, and empowering public-spirited motives [Theory of Moral Sentiments / co-operation].
I had such a blend in mind a few years ago when I had a stab at defining the meaning of business beyond the outworn credo of shareholder value and the Newtonian simplicity of customer-centricity; that is, to establish and drive mutual value creation. Competitive and co-operative.
Information is critical
Posner and Weyl envisage viscerally different markets with the intention, as the corresponding book review in the Economist points out, “to mount an onslaught against market power.” They’re searching for new kinds of market dynamic to eradicate the inequities of existing markets.
This places Posner and Weyl in the company of former World Bank chief economist (and fellow Georgist) Joseph E. Stiglitz, renowned for railing against the unfettered application of free-market ideology. He notes that “one of the reasons that the invisible hand may be invisible is that it is simply not there.” He attributes such absence in part to imperfect or indeed entirely missing information, the focus of the microeconomic field of information economics.
Equal access to information is critical, although each of us has the agency to interpret it as we think best. You and I have our own unique situation in the world, our own private reflections on it and relations and actions within it, which leads to you and I valuing things differently. This is perfectly natural. As Posner and Weyl put it:
People’s valuations are private information; the genius of the market is its capacity for disseminating this information from consumers to producers through the price system.
Information is transmitted. Privacy is respected. Social knowledge is created. But this market process has some serious shortcomings very pertinent to our context here …
Price ≠ value
Cynics believe people are motivated purely by self-interest rather than acting for honourable or unselfish reasons, and an Oscar Wilde character describes a cynic as “a man who knows the price of everything and the value of nothing.” In resisting such cynicism, George Monbiot writes that “putting a price on the rivers and rain diminishes us all”, most apt when reflecting on the data deluge.
Same dollar, different value
Money has diminishing marginal value (the more you have, the less you value an extra dollar), and so how can price alone be welfare-maximizing? Even if you don’t subscribe to the welfarist agenda, it’s hard to ignore the implication that, as inequality rises, the price mechanism may do a worse job of allocating resources, potentially compounding rentier capitalism.
Price is informationally meagre
Price entails massive information loss. For example, why does Alice buy at that price and yet Bob does not? Was the product available to Claire? Why did Dylan determine the product doesn’t meet his needs? How can Ellen tell if the price encompasses all the societal and environmental costs? How might Fabio have faith in the after-sales relationship?
Answers could be sought when markets were predominantly conversations, but financialization has obscured such insight.
In The End of Money and the Future of Civilization, Thomas Greco writes that the nature of money has changed profoundly over the past three centuries unbeknownst to many and that it has become an instrument for centralizing power, concentrating wealth, and subverting popular government. But it remains about as informationally meagre as ever.
In response, open money has been the life mission of my Digital Life Collective colleague Michael Linton. Fiat money is dissociative whereas open money is connective, relational, social. It confers no power of one over another, only one with another. Linton and Greco first defined money as “an information system we use to deploy human effort” in the Whole Earth Review of Summer 1987, and as David Boyle notes in his book The Money Changers, open money is “primarily a function of information — providing information about value so things can be exchanged …”
Where conventional money flows erratically in and out of our communities, creating dependencies that are harmful to the economy, society and nature, open money designs to re-circulate to enable business and trade.
I’m no economist — I study the nature of information not wealth — but for me, any such device that seeks to grow the informational content describing a transaction, indeed any interaction, and maintains the objective of sustainable human flourishing at its heart, is a move in the right direction. Quite simply, we need all the informational richness we can get …
It’s complex
Stafford Beer articulates the law of requisite variety as “variety absorbs variety”. In short, this means we need as much information in the control mechanism as the system we seek to control. Complexity exhibits itself as a random mix of order and chaos, and it would be invaluable then to encourage the conditions for sufficient emergent governing to maintain a liminal systemic plateau.
Gregory Bateson used the word ‘plateau’ to designate something very special: a continuous, self-vibrating region of intensities whose development avoids any orientation toward a culmination point or external end.
In other words, “the intensity is a goal-in-itself, a situation of constant evolution and becoming in which conflict does not build, but is expressed and released.” Such a desirable dynamic requires more information than expressed in a two-for-one deal! It is impossible to sustain such systemic plateaus with little more than the acute informational paucity that is price.
The requisite information exchange assists a process of interaction, in its multiple variables, that produces a mutual learning context — which just happens to be the very definition Nora Bateson gives the verb form of the neologism symmathesy. Such process is so critical it warrants its own word.
Competitive markets may offer a simplistic mechanism for navigating complexity for finite periods, but we’re far from entertaining the end of the associated undesirable extremes. If the very concept of market can be bent to new design, we need a re-framing that embraces massively richer information flows that accommodate, inform and encourage both competition and co-operation.
(Inter)personal data isn’t like the information we call money, nor is its interpretation akin to price. Money is comparatively simplistic. Transactional. Binary. By distinct contrast, (inter)personal data is more like the rich, varied and complex information flows present in rainforests, in oceans, in human cultures. Its value far exceeds a market price; a market price would only strangle its value.
The radical, systematic, information rich combination of competition and co-operation is tantalizing and reminds me of Gandhi’s provocation:
The difference between what we do and what we are capable of doing would suffice to solve most of the world’s problems.
Now we just need to get that information flowing appropriately …
The misleading name, metaphor defiance, and awesome potential of “personal data”
When talking human systems and human data, it’s not long before the GDPR makes its presence felt. According to the regulation, ‘personal data’ means:
any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.
The innovation, indeed transformation, I’m talking about here is in the spirit if not the letter of the regulation. Perhaps the most prominent and simplest explanation for why it cannot be to the letter is contained in the Regulation’s first definition (Article 4) by which “an identified or identifiable natural person” is thereafter known as a “data subject” — entirely the wrong framing.
Moreover, nearly all data that meets the regulation’s definition of personal relates in fact to two or more persons / parties — consider that lunch date, that genome map, those photos, that bank account. Furthermore, the person is defined by the group, and the group by its participants, so the I and the we are inseparable. Whereas current technological designs for personal data have it sitting with and transferring between nodes (individuals and organizations including mediators), I’m thinking in terms of the data defining and ‘residing’ on the edges (relationships).
I’m looking at humans not mere data subjects. I’m looking at digital humans. I’m looking at the dignity of digital humans including their agency and privacy. And rather than each of us in isolation, I’m looking at digital humans living and working together directly and indirectly and achieving amazing things. My confrère Mihai Alisie stepped up and labelled what I am in fact looking at. Interpersonal data. And by interpersonal I’m including groups and organizations (and cyborgs!) as persons.
A few weeks later, I introduced the concept in a series of posts on the AKASHA Foundation blog:
The misleading name, metaphor defiance, and awesome potential of “personal data”.
Metaphor defiance
The title of the blog series includes reference to metaphor defiance, and here’s what I mean by that. While accepting that language is metaphor, there doesn’t appear to be a wholly useful metaphor that can apply to anything but a fragment or two of the required data architecture. It seems that there’s never been anything like personal or indeed interpersonal data. No metaphor can be extended to analogy that’s for certain.
I’ve looked at data-as-property, data-as-labour, data-as-reputation, data-as-public-good, and data-as-me. All are useful in learning about the nature of data, but I conclude that they’re all far from ideal, or indeed practicable, to varying degrees. Sticking too tightly to a pre-digital metaphor may not just be fruitless, it could produce more problems that it addresses. In short, there is no other way to say it other than data is unlike anything else. Data is data.
I will address the property and labour metaphors without repeating too much from my earlier posts.
Data as property
From observation, the idea that we can treat personal data as property is currently the most widely held conceptualisation of those who have given it any thought at all. The idea spreads in reaction to the growing awareness of and deep concern for the business models of the likes of Facebook and Google — so-called Surveillance Capitalism. But personal data is not property. We cannot own it like property.
Unlike property, it isn’t rivalrous and should in fact be anti-rivalrous in many contexts (where the value is all the greater the more it’s shared around). Indeed, it is of little or no value to anyone in isolation — data needs other data to acquire the potential to become information, and information needs other information to acquire the potential to crystallize as knowledge.
Now ask yourself, do you really own your image, or the fact that you have roots in a specific ethnic group? No. Nevertheless, you should expect not to have this information used discriminately. Your image is one thing; its capture, analysis and association with your identity (facial recognition) is quite another, let alone the actual application of such information. Asserting that data isn’t property doesn’t mean you relinquish all agency over it, but it does mean that “control” is the wrong way to think about it — such control cannot scale.
In aiming for brevity, here are two visceral conclusions on the subject. Martin Tisné argues that the idea of data ownership is a category error with pernicious consequences, and the European Data Protection Supervisor dislikes these consequences so intensely it likens a market for personal data to a market for live human organs. Personal data is not property, and we shouldn’t even try to treat it like property.
Data as labour
On the face of it, the connection between data-as-labour and data-as-property is easily made.
A market is a system for the exchange of goods and services, including labour. One can only offer for exchange that which one owns, and one owns one’s labour. These are different types of ownership — owning a tractor and the crops it helps nurture is different from owning the very hands that operate the tractor for example — but if this distinction is irrelevant, data-as-labour very quickly looks like a rebadging of data-as-property replete with its fatal flaws.
Radical Markets grapples with a closely related poverty of outcome in a different context. Our current property ownership paradigm is predicated on the Lockean labour theory of property whereby property originally comes into being by labouring on natural resources. Were you first to labour the land? Yes, then that land is yours. As the Property is Monopoly chapter points out, this has led to some stubbornly unfair outcomes. We shouldn’t then pursue anything that looks like a ‘data labour theory of property’.
Nevertheless, philosophical theories of labour, of work, do imbue such activities with different meaning. I don’t discuss these at length in my three blog posts because, well, they’re blog posts not a doctoral thesis. I offer up more detail here by pointing you to last year’s DECODE project report Data-driven disruptive commons-based models (pp25–36), which presents a thorough yet reasonably accessible deeper dive into the labour aspects of the digital economy. The report concludes that the conception of data-as-labour with which the RadicalxMarket community is familiar denies “the intrinsically collective dimension of value and wealth created by Internet users through their interactions in a network economy.”
This corresponds to two points I raised earlier. First, collective and network economy accord with my stressing co-operation not just competition. Second, value tallies with my assertion that the potential here far exceeds a market price. The data and algorithmically-derived insights are the product of commons, of community, and the challenge then is to have that value circulate back into the community (quoting Casilli 2015), which should remind you of the intention for open money of course.
If you want to continue to think in terms of labour, perhaps co-operative labour is more apt. And there is no labour market so to speak because such co-operation is non-rivalrous, and indeed likely anti-rivalrous. It is in fact not too distant from some of the original hopes and dreams for the societal contribution of the Internet and World Wide Web.
Interpersonal data — decentralized by nature
In a blog post for the World Wide Web foundation, I describe decentralization as a deep cause of causes you care about deeply.
The ultimate information technology challenge is the care and maintenance of a digital infrastructure that can help us rise up to so-called super wicked problems, collectively. Given the growing appreciation of the nature of complexity and the complexity of nature, we know we’re in the domain of systems thinking and sustainability — the health and resilience of living systems including our planet, our societies, and our organisations.
Sustainability requires healthy, distributed networks, with both diversity and individual agency, to facilitate the emergence of collective intelligence. It is these qualities our digital technologies must enable and encourage.
When considering the sociotechnical architecture for personal data — to create socially valued outcomes from the digitalized flux of our living, labouring, learning, creating, co-operating, competing — we can contemplate a design along the lines of the oligopolistic mediated status quo, or a design that requires, encourages and supports decentralization. It’s clear which of these I work on and why.
To me, the goal of initiatives such as The Data Union, as well-intentioned as it may be, is defensive rather than creative, constraining rather than liberating. Unfortunately, if they are successful in unionizing users of the major digital platforms, they might well help lock-in this very early status quo in commercial terms making the exploration of alternative more valuable and, I would argue, existentially necessary constructs all the more challenging. The more attention we pay to architectures that effectively respect the binary simplicity of them-and-us — of gargantuan data corporates .v. individuals — the harder it will be to seed and nurture what I would describe as a more natural design. We may well find ourselves with the mother of all Nash equilibria.
The situation shares much in common with the current trajectory for data-driven agriculture. When the collation and sense-making of farming data is in the hands of a few powerful IT companies, every farmer becomes a tenant farmer whether or not they managed to secure some ‘compensation’ in the process. (I’m involved in a project engineering an alternative to this otherwise inevitable outcome — do get in touch if this is your field, pun intended.)
Interpersonal data architecture is neither individualistic nor collectivized. The data surrounds us at all scales, nesting and interpenetrating, privacy-preserving and socially meaningful. ‘Intelligence’ is invited to the data rather than have the data go to the ‘intelligence’. It is distributed and disintermediating. And it is about mutual value creation not property rights.
I feel I know what the authors of Radical Markets and Jaron Lanier (who discussed the data-as-labor idea in 2013) are striving for, but it appears we have different ideas for exactly what and exactly how. Nevertheless, all dissonance offers potential resolution, and perhaps that might entail taking the subject of the data chapter deeper so to speak, amplifying the potency of the other chapters in the process, and those yet to be written.
For a start, we share a desire to locate our designs somewhere between the extremes of fanatical individuality and the total subjugation of the individual in favour of the collective. It feels to me that this is compatible with the majority view, at least in terms of the European cultures with which I’m most familiar.
As Hitzig and Weyl write in a recent paper:
… Radical Markets are not based on welfarist values, nor are they in pursuit of a wholly individualistic notion of autonomy. … Radical Markets are both means and ends to fostering dynamic collectives and individual freedom to move about them, securing a greater degree of equality and a greater diversity and depth of collective organization than today’s standard capitalist institutions could bring about.
They continue:
… Radical Markets is far from fully instantiating these ideas, and even contrasts with them in some ways. For example, the primarily individualistic focus of [the chapter on] Data as Labor is in tension with the above ideas.
The cause of this tension is to be found I think in locating the challenge and opportunity of personal data on the wrong ontological plane. We can change the plane and relieve the tension. If ontologies aren’t your thing, I can translate this as:
Markets don’t transform personal data so much as interpersonal data transform markets.
In effect, personal data shouldn’t just sit in or on a market. Interpersonal data moves underneath the market (or indeed an expansion of what might be considered a market as discussed above).
AKASHA
We believe that humans are not problems waiting to be solved, but potential waiting to unfold. This is why we nurture projects helping individuals amplify their potential through open systems that expand our collective mind at local, regional and global scales.
The first sentence of the AKASHA Foundation purpose statement is taken from Frederic Laloux’s Reinventing Organizations. The technical architecture for interpersonal data (when we get there) will extend innately to all human systems not just markets, expanding both individual agency and the emergence of a new home of mind. Well that’s the plan anyway!
Our labour, and our work, and our actions (per the human condition) enable and fuel the transformation and revitalization of markets. Radical.
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