Morality and the algorithm

July 7, 2015

In light of recent events in Greece, last week’s post by Emilio Marti seems strangely prescient. Emilio called for more research on the wider societal implications of financial markets: how, he asks, are people ultimately affected by financial markets? How is financial innovation impacting actual investors?

Emilio’s call is part of a broader call for a political turn in SSF. His arguments build on a terrific paper with Andreas Scherer, forthcoming in the Academy of Management Review, as well as numerous comments made in the past by Fligstein, Williams, Roscoe, and others. The AMR piece charges that existing analyses of HFT within the social studies of finance (Beunza and Millo 2014, as well as others) fail to address the problem of social justice. As they see it, sociologists of finance reinforce a technocratic system — different from economists in their sociological interest in stability, but servants to “the system” nevertheless.

Flattering as it is to have my work critiqued on AMR, I respectfully disagree with the diagnosis. As I see it, the transformations that are entailed in High Frequency Trading — the displacement of traditional floor intermediaries by algorithms — turns existing conceptions of what is fair on its head, calling for an entirely new critical agenda. And this is not just about finance. Algos are not just taking over trading, but also hiring, teaching and a wide range of other activities in society. The implications of my argument about ethics and financial algos, I hope, extend to all these industries.

HFT and the need for a new critical agenda

First, why do Marti and Scherer argue that sociologists are reinforcing technocracy? They make three points. First, that research ought to examine the consequences of the rise of HFT for income distribution, especially as it between those who benefit from the high compensations that an expanding financial sector affords and those groups outside of the financial sector that lose in relative or absolute terms. Second, that the design of financial regulation ought to financial regulation should include all affected societal groups, as anything else will only reflect the views of the elite members of society — regulation, again, for the one percent. And third, that financial innovation like HFT might not actually add any value, causing them to worry about where all this money flows (into the compensations of some financial-sector employees) and how this money increases income inequality. In short: existing regulation is technocratic, elitist and protective of tricksters… and sociologists of finance

I disagree on all three counts. I believe examining the distributive consequences of HFT regulation is an unnecessary burden, as governments have multiple levers at their control to achieve different societal goals. If a policy change (e.g. trade liberalization) generates so much welfare gain that the losers could in principle be compensated by the winners (by e.g., tax transfers) then governments should go ahead with it. That bigger pie can then probably be better redistributed with other tools.

Second, including non-professional traders in the regulation might sound like a good idea, but I do fear that the wider public might not add much. Some issues call for technical training. Even Habermas has changed his views on this, and his later work argues that the technically untrained public may not be able to have a productive voice in dialectical deliberation. What I would argue below, instead, is that the regulation of financial innovation (and automation in general) needs to include the voice of the profession that is being displaced.

Third, and most importantly, Marti and Scherer have not touched on the most critical concern raised by automation: ethics. What my research with Yuval shows is that finance is no difference from what Lawrence Lessig has found in other instances of automation. Automating, he found, replaces the old informal norms by the hard rules of computer code. In this process of translation, automation gives technologists a lucrative loophole: the ability to do things legally that were previously shunned as inappropriate by the community of industry actors. In the case of HFT, for instance, their work replaces traditional market-makers on the floor. But whereas the latter were subject to informal norms of proper behavior – the obligation to step in during spells of illiquidity, etc. – the new ones are not. As Yuval and I show in our paper, this contributed to the Flash Crash. Yet the Flash Crash is just one manifestation; there are many other socially destructive algorithmic trading activities that are not illegal. One is latency arbitrage, masterfully described by Michael Lewis. Another one is spoofing. And it is no coincidence that the defense of Sarao, the devil genius spoofer from Hounslow (London) was “I did nothing illegal.” And so forth.

The real problem with automation

The problem goes beyond finance. Whether it is in the music, book selling, dating or the taxi industry, automation has created a social distance between industry participants, potentially turning markets into a Hobbesian nightmare of “every algo for itself.” The original goal was to reform society through the codification of behavior, hoping that its constraining effect would purify dirty business practices. In reality, codification has afforded new types of opportunism and eliminated society’s ability to control opportunism through informal means, as it had traditionally done. This goes back to Durkheim’s core sociological insight: the existence of an informal basis for formal contracts. Put differently: that formal law, by itself, cannot govern a society. The contemporary problem is this something like, computer code, by itself, cannot govern society.

Hence the title of this post – ethics and algorithms. The distinction between norms and rules is at the heart of ethical behavior at work. Norms and principles, rather than legal standards, are what differentiates ethical from unethical. Put differently, automation runs the risk of entailing moral abdication to the computer code.

To make matters worse, technologists make a moral case for automation. See for instance, this proposal to replace HR departments with algorithms. It starts from moral denunciation that human hiring is discriminatory, and HR departments incompetent. Fueled by such well-crafted moral outrage, a lay audience will gobble up the huge and obvious canard that an algo can seriously assess someone’s expert skills. In fact, more recent evidence suggests that algos can even engage in racially offensive profiling on a level that only seriously racist humans would be capable of. Or get very close to gender discrimination, for that matter.

A challenge to sociology

At the core of moral denunciations like the above lies a dangerous ideology, going back to Adam Smith. The idea, often put forth by social scientists in economics and behavioral economics or psychology, is that social structure is the root of all evil. The business people that have dinner with each other? Surely they are price-fixing. Those traders on the pit? They probably are colluding, otherwise why would they be nice to each other. Taxi drivers? All corrupt, but an app will see them right. This demonization of social structure is a direct challenge to sociology and its ability to see nuances, that is, structures (roles, patterns of ties) that are positive and others that are negative. It is sociologically naïve, false, and dangerous — the equivalent of sociologists thinking that all commercial activity is unethical.

By the same token, sociologists should not demonize automation either. Their challenge is to be able to distinguish between good and bad automation designs. In our paper, Yuval and I identify automation designs that draw creatively on the original social structure of the market (allowing, for instance, for human market makers on the trading floor to take over from the algos in times of crisis). Others call for incorporating reputation mechanisms into algorithmic trading.

In sum, as automation becomes widespread, sociologists face what is probably the biggest challenge to their discipline in more than a century, akin to the move from the village to the city that spawned the birth of their discipline in the 19th C. Markets are being redefined. Economic interaction is shifting online, onto algos. Second Life, that failed and boring video game-like virtual society, is actually happening for real in the stock market and other industries, although with no fancy 3D graphs. Traders have, in effect, created avatars of themselves that are interacting in an invisible limbo, with rules that the HFT entrepreneurs mostly came up with, or shaped through lobbying, and with limited control by the rest of society. It’s this barely-visible world that sociologists need to make visible and problematize.


9 Responses to “Morality and the algorithm”

  1. Nathan Coombs Says:

    Thanks for drawing attention to this paper Daniel, which is right up my street given my current research on the regulation of financial algorithms.

    Marti and Scherer make a number of apposite points about the narrowing scope of financial regulation over the past 40 years. And they are right to draw attention to the alienating effects of technical jargon upon public deliberation about the purposes of regulation.

    When these observations are mobilised against SSF, however, due to SSF’s alleged complicity with these trends, I think their argument is a bit tendentious. Essentially, they recycle the well-honed critique of SSF that the field fails to contest critically (and hence normatively) the neoliberal model of governance. While there is a certain amount of traction in this critique, insofar as SSF leans more towards value-free empirical sociology, I can’t really see the specific connection between the handful of SSF papers on high-frequency trading and Marti and Scherer’s argument.

    Their argument seems to be that because SSFers don’t talk about distributive justice when discussing HFT they are by de facto reinforcing technocratic regulatory norms. A bit of a stretch, but ok they may not be wrong strictly speaking. Yet the problem here – which is what is animating a new wave of SSF studies of HFT – is that it remains unclear what justice might mean in the context of HFT,or what the issue is being addressed by regulatory initiatives in this area. Part of the reason for this is that we still don’t have enough knowledge about the practice, the technologies made use of, and existing regulatory responses. It would be easy to believe, tapping into US public outrage about HFT, that what is being discussed is somehow given. But in truth the question of what HFT is, and to what extent it differs from algo trading made use of by almost all market participants, remains controversial. Such issues seem more than enough to occupy SSFers at the current stage of research without being lumbered by the imperative to ascend to the level of normative theorising about the justice-effects of HFT.

    So, for instance, I see my own work on the algorithm tagging regulatory innovation (in the German HFT Act and MiFID II) and the public-private knowledge practices enabling regulators to introduce this device, as providing an important window into what’s actually going on. Maybe this is just to subscribe to the ideology of ‘expert knowledge’ that Marti and Scherer decry; or maybe, as Daniel observes, to assume that expert knowledge can be obviated in calls for more democracy or to ‘get political’ is just a comforting illusion.

    Another possible weakness with Marti and Scherer’s argument is that there is an awful lot of debate going on under the umbrella of ‘efficiency’ and ‘stability’ that they subsume within the technocratic regulatory discourse. One question we debated at the HFT workshop in Konstanz recently, for example, was whether HFT was regulateable per se. Do automated trading technologies and the representational tools relied upon by market surveillance suffer from an intrinsic epistemological dilemma which renders them impotent? Are presumptions of intentionality complicated by automated markets? Do some of technologies made use of by HFTs such as neural nets render the behaviour and interaction of algorithms unknowable? Can regulators make use of all the vast amount of data they are now collecting after the crisis? None of these questions challenge what Marti and Scherer consider the technocratic discourse concerned with efficiency and stability, but they do provide the focus for real and ongoing political debates surrounding the regulation of the financial sector. In my opinion, if SSF can make an impact anywhere, it is by informing and making interventions into these meso-level regulatory debates.

    This is not to say that SSFers should surrender the big questions or avoid the big critical project of asking what *should* regulation be doing – what its ultimate goal should be – but the methodologies of SSF are not necessarily best suited to addressing these questions.

    • Emilio Marti Says:

      Thanks for this comment, Nathan! In our paper we do not criticize any specific school of research (such as financial economics or the SSF). Rather, we criticize the overall state of research about financial innovations. We argue that financial regulation is in serious trouble if no researchers investigate the distributive implications of financial innovations and other implications that matter for justice. This is how I see the current state of research around HFT.

      If so, this raises the question of which researchers are in the best position to illuminate some of these justice questions. Indeed, I do not see the SSF as the spearhead in this endeavor. In our paper, we rather talk about how organization researchers with an interest in institutional complexity and power could help explore whether financial innovations – mediated through their impact on financial and non-financial firms – increase income inequality.

      However, while SSF scholars may not be the ringleaders in this endeavor, I still think that SSF scholars could use their distinct methods to make important contributions to this collective endeavor, thereby complementing insights from (critical) organization theorists. For example, as discussed in my post last week, SSF scholars could illuminating how new tools help financial service firms sell ever new products.
      Beyond this, contributions to questions of efficiency and stability remain important. The point in our paper is not that these questions are irrelevant, but that researchers should ALSO discuss questions of justice. So I see the issues you discussed in Konstanz as highly relevant!

      I’m looking forward to hearing more about your work on financial regulation in the context of the German HFT act!

  2. Reblogged this on Crowd Thought en el Mercado de Capitales Chileno and commented:
    Great post about algorithms, SSF and beyond

  3. Emilio Marti Says:

    Thank you for this excellent post, Daniel! I am intrigued by your third point. In my post, I argued that the SSF and other social scientists should try to illuminate the bigger picture around financial innovations such as HFT. You do exactly this, but in a different way than I had in mind. You illuminate the bigger picture by showing that algorithms are not only relevant in financial markets, but also in many other sectors and areas (hiring, dating, book selling, etc.). You criticize the “demonization of social structure” and the lack of nuance about how certain roles and ties can actually be beneficial for society. This is spot on! With this, you enlarge the picture around financial innovations by taking into account automation in different sectors and areas.

    By contrast, in my post last week, I argued that researchers should analyze whether and, if so, how financial innovations contribute to rising compensations within the financial sector that contribute to rising income inequality. For me, illuminating the bigger picture around financial innovations meant to cross different levels of analysis within one sector (the financial sector). Enlarging the picture around financial innovations can thus happen both vertically (as you do, by looking at different sectors, but staying at the same level of analysis) and horizontally (as I propose, by looking at different levels of analysis, but staying within the financial sector). Both are highly important!

    I am less convinced by your first and second point, though. Your first point refers to what economists describe as the second fundamental theorem of welfare economics. This theorem assumes that questions of efficiency can be discussed in isolation from question of distribution. I have major concerns with this theorem, but this would be a longer discussion… However, even if we assume that this theorem is applicable for our context, we would still need to generate knowledge about the distributive consequences of HFT. Otherwise, how can you know how much the winners should “compensate” the losers? So, even in the pipe-dream world of economists, we would still need knowledge about how HFT influence income distribution.

    As for your second point: Technical expertise – including the expertise of professions that get displaced – is often very important. I fully agree. However, no technical expertise can clarify the question of what to do about the trade-off between efficiency and stability that we see around HFT – for the simple reasons that this is not a technical, but a political question. In our paper, we therefore propose “deliberative polls” as a way to include different societal groups in a debate about what financial markets should do for society.

  4. danielbeunza Says:

    Hello Nathan, what a terrific defense of the postulates, approach and normative stance of SSF. I would be very interested to know more about that work on algorithm tagging regulatory innovation, which sadly I missed in Konstanz. All four questions you ask are absolutely essential to address, and only sociologists have the tools do to so. Looking forward to hear of your answers!

  5. danielbeunza Says:

    Hello Emilio, thank you. It is a great question: does financial innovation make the rich richer? The paradox is that financial technology (so-called “fin tech”) is now widely perceived to be the ultimate solution to the fat margins in the financial sector. New financial technology, the promise goes, will disrupt banks by simplifying tasks, putting them on an app and giving power to the people. There are reasons, in other words, to think that innovation will benefit “the people” as much as there are to think it won’t. Fintech has now become the largest receptor of venture capital funds in all areas of start-up companies in London. What a terrific site to answer this question empirically

    • Emilio Marti Says:

      Thanks, Daniel! Yes, “fintech” innovations are interesting. However, I have some concerns about general statements on whether “financial innovation” (in its singular) is beneficial for society or not. Financial innovations range from the ATM, to CDSs, microfinance, and fintech. Can we really make an overall assessment of all these financial innovations? I doubt it.

      I therefore prefer taking about “financial innovations” (in the plural). I can thus acknowledge that some financial innovations (such as “fintech”) may indeed be beneficial for the poor. Meanwhile, other financial innovations (such as HFT) may primarily benefit the rich.

      I would thus slightly rephrase your question as: do SOME financial innovations make the rich richer and, if so, which ones and how? Besides this reformulation, I fully agree that this is a terrific site for research!

  6. […] Related article: Morality and the algorithm […]

  7. danielbeunza Says:

    Agreed. E.g., just yesterday, one of the founders of Abundance, a crowdfunding company presented his firm to my students in the summer school course in SSF:

    Because the company accepts investments of as low as five pounds (eight dollars), the company can be read as a way to allow the poor (or at least the non-wealthy) to become a financier.

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