Who counts?

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I had been looking forward to reading The Ordinal Society by Marion Fourcade and Kieran Healy, and it hasn’t disappointed. My copy is covered in sticky notes marking interesting points.

What do they mean by the term? It is the world created by Silicon Valley built on the digital traces we all create using its services and that “stratifies individuals through a myriad of differentiated methods of matching, scoring and classification. Those methods have both a practical application and a moral valence. The ordinal society is both a means of social organization and a mode of first person experience.” The book brings the lens of sociological theory – Mauss, Deleuze, and Bourdieu feature most prominently – to the now-familiar effects of digitalization.

This makes it orders of magnitude more persuasive than the dreadful but influential Age of Surveillance Capitalism of Shoshana Zuboff. The Ordinal Society traces the evolution of digital capitalism from the initial stages of a gift exchange structure – it was all free and so wonderful nobody much minded the gradual development of data extraction business models – through the subsequent collection and use of data, development of classifications and then the increasingly extractive and financialized business models of digital platforms. The classification of individuals and association of these categories with profits created rankings of people which almost unavoidably turned into apprasial of relative moral worth. “The data imperative was a cultural and political accomplishment, beyond the economic search for efficiency.” The data brokerage business is one of the most dynamic but also least well-understood of modern industries – including on the dark web. And while “social life is messy”, the digital world needs orderly (binary) constructs. These lead to decisions that are hard to challenge – one’s digital identity might have been denied right of entry to another country before the flight has taken off. The book coins the term ‘eigencapital’ (in a broad analogy to cultural capital) to capture the concept of the data asset that is one’s individual identity (based on the eigenvectors of information constructed from a dataset).

What use is your eigencapital? One example the book gives is that it is embodied in the amount of time people need to wait to access services. Higher eigencapital people might get access to a better-staffed phone number and call centre, or get a place higher up the queue for a chatbot to respond: “Sociologists have long studied queues and lines as structures that allow for both control of status.” (I’d never thought of this but it makes sense of my mill-worker parents’ obsession with never being late for anything ever.) Queuing data amplifies this social dynamic.

Anybody sentient in the UK in recent weeks will have become aware of the Post Office Horizon scandal: the very existence of digital data makes people believe in its truth (and there is a dreadful – albeit rebuttable – presumption in UK law that computers are right and people are wrong). The use of machine learning systems and now generative AI is massively amplifying all the ways this can lead to unfreedom and injustice. “At their best these approaches really do perform astonishingly well. But their scale and complexity invites a kind of deference to the oracle, both in terms of its care and feeding on the one hand, and its pronouncements, on the other.”

The sections on digital identity and money reminded me of Dave Birch’s wonderful book Identity is the New Money. Dave had tremendous foresight; the issues he raised then are currently key issues in the debate about Digital Public Infrastructure. The Ordinal Society has examples like the collection of sensor data from cars turning auto manufacturers into financial services companies, and the infamous John Deere case. ‘Servitization’ is becoming more widespread across the economy although to what extent is unclear. The book also has a section on crypto and DAOs, an area I’ve never really got to grips with.

It ends pessimistically: “Public goods and collective goals are being dissolved in the acid bath of individualization and competition, leaving us increasingly alone in a hyperconnected world whose social ordering is precisely metered and, in its factitious way, inarguably ‘right’. Life in the ordinal society may well be unbearable.” Funnily enough, I ended up less pessimistic, if only because this would be so unbearable the revolution would happen first. Still, we have a lot of work to do to force the toxin of data and engagement-driven business models into retreat.

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Language in the always on world

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The house is full of slightly random books. I picked up one sent to Rory, Because Internet: Understanding how language is changing by Gretchen McCulloch, a few years old now. She’s a linguist and the book is about the way being online has been changing everyday use of language. It was a surprisingly enjoyable read, very engagingly written and full of interesting observations and ideas. A few examples:

“We’re all writing for the unblinking eye of Data.” Even truer now in the era of LLMs.

The idea of ‘context collapse’ from danah boyd. A new phrase to me: “[a] term for when people from all your overlapping friend groups see all your shared posts from different aspects of your life.” The opposite of a concept I’ve found useful, ‘privacy in public’, when in the offline world we would willingly share sensitive information with certain other people but not everyone, and not in a way that different bits of information could be joined up. For instance, my GP is welcome to know details about my health but Palantir not so much unless I’m sure they can’t do me damage with it.

Ray Oldenburg’s concept of ‘third places’ (from his 1989 book The Great Good Place), social spaces where you can meet and chat with others. The local cafe or pub, the hallway at a conference. (One thing not I’ve not seen much discussed in the hype and counter-hype about Jonathan Haidt’s new book is that one reason teens are online so much is that modern society has removed all the places they used to be able to hang out in.)

And my favourite: texts and textiles have the same root, the indo-European teks (‘to weave’). (So does technology.) I’m from a Lancashire cotton mill family and thought that was why I collect blankets, towels, fabrics. It turns out that and accumulating books are essentially doing the same thing.

Others would surely pick out other things. There are chapters on emojis, memes, tone online and much else. A very nice book for anyone who likes words.

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Money, money, money

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Money has always seemed mysterious to me, and so I’ve always carefully avoided monetary economics as too difficult (which makes it ironic that when I returned from my US PhD programme to a job in the UK Treasury in 1985 I was assigned to the monetary policy unit – this in the days long before Bank of England independence, when the Treasury and Chancellor made the policy decisions). Still, from time to time I dip in, and found Stefan Eich’s The Currency of Politics: The Political Theory of Money from Aristotle to Keynes an interesting read.

The book is an intellectual history of how certain key thinkers regarded money, covering Locke, Fichte and Marx in between Aristotle and Keynes. The selection is used to illustrate a core point that how money is theorised and governed involves political choices, not technocratic ones. This repeated theme reminded me of Paul Tucker’s Unelected Power, similarly arguing against seeing monetary policy as an expert domain. I was initially resistant to this but on reflection at least partly agreed (partly first, because the technical affordances set the boundaries of policy feasibility, and second, because I hold on to the idea that most ‘experts’ in such areas are motivated by a sense of the public good rather than ideology or personal philosophy).

Anyway, back to this book. Each chapter reflects a close reading of the relevant work of each subject combined with an analysis of the contemporary political context. Thus he argues that Locke, for example, in his contributions to the debate about England’s increasingly clipped silver coinage, made the political move of ‘depoliticising’ money, arguing for its ‘intrinsic value’ linked to a quantity of metal: “Locke’s intervention was itself political, even where it removed political discretion,” with the aim of bolstering the role of the state as a general guarantor of (classical) liberal freedoms while limiting its scope to act in detail.

Similarly on Keynes, he writes, monetary policy, “was a public task tied to social justice. It derived its legitimacy from the implicit political covenant that also grounded the state. But it was nonetheless removed by at least one degree from popular politics since it relied on management by a group of experts who had to carefully navigate between democratic legitimacy and the political uses of their expertise.” This seems spot on. And the act of navigation is challenging in turbulent times. Independent central banks have broadly done a good job of stabilising the aggregate economy since the mid-2000s but a bad job in not recognising the distributional and political consequences of QE on a massive scale.

The other message I took from the book was that political and ideological contention both contributes to the emergence of new monetary technologies and is channelled by the affordances of the technologies. When I worked in the Treasury, my job was basically to try and figure out why monetary aggregates were growing so damn quickly – this was the tail end of the pure monetarist experiment in the UK. It turned out that trying monetary targeting at a time of huge technological change (derivatives markets exploding, ATMs and credit/debit card use spreading rapidly, deregulation of consumer credit) was doomed to failure. I still don’t understand cryptocurrencies but they are certainly part of this continuing dialectic of  – to mix metaphors horribly – walking the tightrope between the inevitably political character of the monetary system and the desirability of stability in the economy which requires taking it out of politics. The Currency of Politics really helps understand this.

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Unaccountable

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I read a proof of The Unaccountability Machine by Dan Davies with a view to blurbing it, and was more than happy to recommend it. This is a fascinating book. The subtitle indicates its scope: “Why Big Systems Make Terrible Decisions and How the World Lost Its Mind”. The book asks why mistakes and crises never seem to be anybody’s fault – it’s always ‘the system’. Davies uses the concept of the ‘accountability sink’ – a policy or set of rules that prevent individuals from making or changing decisions and thus being accountable for them. He writes: “For an accountability sink to function, it has to break a link; it has to stop feedback from the person affected by the decision from affecting the operation of the system. The decision has to be fully determined by the policy, which means that it cannot be affected by any information that wasn’t anticipated.” I predict that the more machine learning automates decisions, the more accountability sinks we will experience. Think Horizon. But there are plenty of non-automated examples. Davies cites, for example, Gill Kernick’s wonderful book on the Grenfell disaster (and others), Catastrophe and Systemic Change.

The book draws heavily on Stafford Beer’s cybernetics, providing the public service of digesting all of his writings and making them accessible. Cybernetics was of course concerned with using the flow of information and enabling feedback. Decisions about how to make decisions are part of the system. Hence the often-quoted principle that “the purpose of a system is what it does” – and not what it says it does. The book has several chapters describing how systems operate, including how to conceptualise a ‘system’ in the complex, messy real world. Davies observes that this requires a representation that is “both rigorous and representative of reality.” The selection of categories and relationships in a system is a property of the choices about description and classification made by the analyst rather than inherent reality. He describes – using plentiful examples – how systems so often malfunction.

The book has a chapter specifically diagnosing the strengths but also malfunctions of economics. He writes: “Economics has been a major engine of information attenuation for the contrl system. Adopting the economic mode of thinking reduces the cognitive demands placed on our ruling classes by telling them there are a lot of things they don’t have to bother thinking about. … when decisions are made that have disastrous long-term conseqneuces as a result of relatively trivial short-term cash savings, the pathology is often directly related to something that seemed like a good idea to an economist.”  There’s an interesting section on ‘markets as computing fabric’, a ‘magic calculating machine’. This was echoed recently in some terrific posts by Henry Farrell and Cosma Shalizi. It’s a fruitful way of thinking about collective economic outcomes. I also strongly agree with the sections about collecting the data – classification and data collection is a super-power (as I’ve been writing for years in connection with GDP and beyond). The book says, “Numbers are collected for a purpose and it’s often surpriginly hard to use them for any other purpose.” Moreover, many numbers are not collected, which makes it hard to ‘prove’ claims about the potential for the system to operate differently.

The book ends by returning to system dysfunction – ‘morbidity’. From the toxic idea of shareholder value maximisation to the fentanyl crisis in the US, from the collapse of public infrastructure networks to the advers effects of private equity (which Brett Christophers has dissected forensically in his book), economic and financial systems need a redseign. Davies suggests one step that he thinks would have a big impact: make these investors liable for company debts. Oh, and make sure the economists are not in charge: “Every decision-making system set up as a maximiser needs to have a higher-level system watching over it.”

The Unaccountability Machine does not directly address my current preoccupation, which is the implications for automated decision-making in public services, in particular, of GOF machine learning and generative AI, but is higly relevant to it. It’s a cracking read and I highly recommend it.

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AIs as the best of us

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Another book of many out on AI is As If Human: Ethics and Artifical Intelligence by Nigel Shadbolt and Roger Hampson. I found this a very accessible book on AI ethics, possibly because neither author is an academic philosopher (sorry, philosophers….). Generally I’m a bit impatient with AI ethics, partly because it has dominated debate about AI at the expense of thinking about incentives and politics, and partly because of my low tolerance for the kind of bizarre thought experiments that seem to characterise the subject. Nevertheless, I found this book clear and pretty persuasive, with the damn trolley problem only appearing a small number of times.

The key point is reflected in the title: “AIs should be judged morally as if they were humans,” (although of course they are not). This implies that any decisions made by machines affecting humans should be transparent, accountable and allowing appeal and redress; we should treat AI systems as if they were humans taking the decisions. There may be contested accountability beyond that to other groups of humans – the car manufacturer and the insurer, say – but ‘the system’ can’t get away with no moral accountability. (Which touches on a fantastic new book, The Unaccountability Machine, by Dan Davies that I will review here soon).

Shadbolt and Hampson end with a set of seven principles for AIs, including ‘A thing should say what it is and be what it says’, and ‘Artificial intelligences are only ethical if they embody the best human values’. Also that private mega-corps should not be determining the future of humanity. As they say, “Legal pushback by individuals and consumer groups against the large digital corporations is surely coming.”

As If is well worth a read. It’s short and high level but does have examples (and includes the point that seems obvious but I have seen too rarely made, that the insurance industry is steadily putting itself out of business by progressively reducing risk pooling through data use).

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