Tag: jake goldenfein

We need a theory of collective agency to guide data intermediary design

Last week Jake Goldenfein and I presented some work-in-progress to the Centre for Artificial Intelligence and Digital Ethics (CAIDE) at the University of Melbourne. The title of the event was “Data science and the need for collective law and ethics”; perhaps masked by that title is the shift we’re taking to dive into the problem of data intermediaries. I wanted to write a bit about how we’re thinking about these issues.

This work builds on our work “Data Science and the Decline of Liberal Law and Ethics“, which was accepted by a conference that was then canceled due to COVID-19. In retrospect, it’s perhaps for the best that the conference was canceled. The “decline of liberalism” theme fit the political moment when we wrote the piece, when Trump and Sanders were contenders for the presidency of the U.S, and authoritarian regimes appeared to be providing a new paradigm for governance. Now, Biden is the victor and it doesn’t look like liberalism is going anywhere. We must suppose that our project will take place in a (neo)liberal context.

Our argument in that work was that many of the ideas animating the (especially Anglophone) liberalism we see in the U.S., the U.K., and Australia legal systems have been inadequate to meaningfully regulate artificial intelligence. This is because liberalism imagines a society of rational individuals appropriating private property through exchanges on a public market and acting autonomously, whereas today we have a wide range of agents with varying levels of bounded rationality, many of which are “artificial” in Herbert Simon’s sense of being computer-enabled firms, tied together in networks of control, not least of these being privately owned markets (the platforms). Essentially, loopholes in liberalism have allowed a quite different form of sociotechnical ordering to emerge because that political theory did not take into account a number of rather recently discovered scientific truths about information, computing, and control. Our project is to tackle this disconnect between theory and actuality, and to try to discover what’s next in terms of a properly cybernetic political theory that advances the goal of human emancipation.

Picking up where our first paper left off, this has gotten us looking at data intermediaries. This is an area where there has been a lot of work! We were particularly inspired by Mozilla’s Data Futures review of different forms of data intermediary institutions, including data coops, data trusts, data marketplaces, and so on. There is a wide range of ongoing experiments with alternative forms of “data stewardship” or “data governance”.

Our approach has been to try to frame and narrow down the options based on normative principles, legal options, and technical expertise. Rather than asking empirically what forms of data governance have been attempted, we are wondering: what ought the goals of a data intermediary be, given the facts about cybernetic agency in the world we live? How could such an institution accomplish what has been lost by the inadequacies of liberalism?

Our thinking has led us to the position that what has prevented liberalism from regulating the digital economy is its emphasis on individual autonomy. We draw on the new consensus in privacy scholarship that individual “notice and choice” is an ineffective way to guarantee consumer protection in the digital economy. Not only are bounded rationality constraints on consumers preventing them from understanding what they are agreeing to, but also the ability of firms to control consumer’s choice architecture has dwarfed the meaningfulness of whatever rationality individuals do have. Meanwhile, it is now well understood (perhaps most recently by Pistor (2020)) that personal data is valuable only when it is cleaned and aggregated. This makes the locus of economic agency around personal data necessarily a collective one.

This line of inquiry leads us to a deep question to which we do not yet have a ready answer, which is “What is collective emancipation in the paradigm of control?” Meaning, given what we know about the “sciences of the artificial”, control theory, theory of computation and information, etc., with all of its challenges to the historical idea of the autonomous liberal agent, what does it mean for a collective of individuals to be free and autonomous?

We got a lot of good feedback on our talk, especially from discussant Seth Lazar, who pointed out that there are many communitarian strands of liberalism that we could look to for normative guides. He mentioned, for example, Elizabeth Anderson’s relational egalitarianism. We asked Seth whether he thought that the kind of institution that guaranteed the collective autonomy of its members would have to be a state, and he pointed out that that was a question of whether or not such a system would be entitled to use coercion.

There’s a lot to do on this project. While it is quite heady and philosophical, I do not think that it is necessarily only an abstract or speculative project. In a recent presentation by Vincent Southerland, he proposed that one solution to the problematic use of algorithms in criminal sentencing would be if “the community” of those advocating for equity in the criminal justice system operated their own automated decision systems. This raises an important question: how could and should a community govern its own a technical systems, in order to support what in Southerland’s case is an abolitionist agenda. I see this as a very aligned project.

There is also a technical component to the problem. Because of economies of scale and the legal climate, more and more computation is moving onto proprietary cloud systems. Most software now is provided “as a service”. It’s unclear what this means for organizations that would try to engage in self-governance, even when these organizations are autonomous state entities such as municipalities. In some conversations, we have considered what modifications of the technical ideas of the “user agent”, security firewalls and local networks, and hybrid cloud infrastructure would enable collective self-governance. This is the pragmatic “how?” that follows our normative “what?” and “why?” question but it is no less important to implementing a prototype solution.


Benthall, Sebastian and Goldenfein, Jake, Data Science and the Decline of Liberal Law and Ethics (June 22, 2020). Available at SSRN: https://ssrn.com/abstract=3632577 or http://dx.doi.org/10.2139/ssrn.3632577

Narayanan, A., Toubiana, V., Barocas, S., Nissenbaum, H., & Boneh, D. (2012). A critical look at decentralized personal data architectures. arXiv preprint arXiv:1202.4503.

Pistor, K. (2020). Rule by data: The end of markets?. Law & Contemp. Probs.83, 101.

Notes about “Data Science and the Decline of Liberal Law and Ethics”

Jake Goldenfein and I have put up on SSRN our paper, “Data Science and the Decline of Liberal Law and Ethics”. I’ve mentioned it on this blog before as something I’m excited about. It’s also been several months since we’ve finalized it, and I wanted to quickly jot some notes about it based on considerations going into it and since then.

The paper was the result of a long and engaged collaboration with Jake which started from a somewhat different place. We considered the question, “What is sociopolitical emancipation in the paradigm of control?” That was a mouthful, but it captured what we were going for:

  • Like a lot of people today, we are interested in the political project of freedom. Not just freedom in narrow, libertarian senses that have proven to be self-defeating, but in broader senses of removing social barriers and systems of oppression. We were ambivalent about the form that would take, but figured it was a positive project almost anybody would be on board with. We called this project emancipation.
  • Unlike a certain prominent brand of critique, we did not begin from an anthropological rejection of the realism of foundational mathematical theory from STEM and its application to human behavior. In this paper, we did not make the common move of suggesting that the source of our ethical problems is one that can be solved by insisting on the terminology or methodological assumptions of some other discipline. Rather, we took advances in, e.g., AI as real scientific accomplishments that are telling us how the world works. We called this scientific view of the world the paradigm of control, due to its roots in cybernetics.

I believe our work is making a significant contribution to the “ethics of data science” debate because it is quite rare to encounter work that is engaged with both project. It’s common to see STEM work with no serious moral commitments or valence. And it’s common to see the delegation of what we would call emancipatory work to anthropological and humanistic disciplines: the STS folks, the media studies people, even critical X (race, gender, etc.) studies. I’ve discussed the limitations of this approach, however well-intentioned, elsewhere. Often, these disciplines argue that the “unethical” aspect of STEM is because of their methods, discourses, etc. To analyze things in terms of their technical and economic properties is to lose the essence of ethics, which is aligned with anthropological methods that are grounded in respectful, phenomenological engagement with their subjects.

This division of labor between STEM and anthropology has, in my view (I won’t speak for Jake) made it impossible to discuss ethical problems that fit uneasily in either field. We tried to get at these. The ethical problem is instrumentality run amok because of the runaway economic incentives of private firms combined with their expanded cognitive powers as firms, a la Herbert Simon.

This is not a terribly original point and we hope it is not, ultimately, a fringe political position either. If Martin Wolf can write for the Financial Times that there is something threatening to democracy about “the shift towards the maximisation of shareholder value as the sole goal of companies and the associated tendency to reward management by reference to the price of stocks,” so can we, and without fear that we will be targeted in the next red scare.

So what we are trying to add is this: there is a cognitivist explanation for why firms can become so enormously powerful relative to individual “natural persons”, one that is entirely consistent with the STEM foundations that have become dominant in places like, most notably, UC Berkeley (for example) as “data science”. And, we want to point out, the consequences of that knowledge, which we take to be scientific, runs counter to the liberal paradigm of law and ethics. This paradigm, grounded in individual autonomy and privacy, is largely the paradigm animating anthropological ethics! So we are, a bit obliquely, explaining why the the data science ethics discourse has gelled in the ways that it has.

We are not satisfied with the current state of ‘data science ethics’ because to the extent that they cling to liberalism, we fear that they miss and even obscure the point, which can best be understood in a different paradigm.

We left as unfinished the hard work of figuring out what the new, alternative ethical paradigm that took cognitivism, statistics, and so on seriously would look like. There are many reasons beyond the conference publication page limit why we were unable to complete the project. The first of these is that, as I’ve been saying, it’s terribly hard to convince anybody that this is a project worth working on in the first place. Why? My view of this may be too cynical, but my explanations are that either (a) this is an interdisciplinary third rail because it upsets the balance of power between different academic departments, or (b) this is an ideological third rail because it successfully identifies a contradiction in the current sociotechnical order in a way that no individual is incentivized to recognize, because that order incentivizes individuals to disperse criticism of its core institutional logic of corporate agency, or (c) it is so hard for any individual to conceive of corporate cognition because of how it exceeds the capacity of human understanding that speaking in this way sounds utterly speculative to a lot of fo people. The problem is that it requires attributing cognitive and adaptive powers to social forms, and a successful science of social forms is, at best, in the somewhat gnostic domain of complex systems research.

The latter are rarely engaged in technology policy but I think it’s the frontier.


Benthall, Sebastian and Goldenfein, Jake, Data Science and the Decline of Liberal Law and Ethics (June 22, 2020). Ethics of Data Science Conference – Sydney 2020 (forthcoming). Available at SSRN: https://ssrn.com/abstract=

“Private Companies and Scholarly Infrastructure”

I’m proud to link to this blog post on the Cornell Tech Digital Life Initiative blog by Jake Goldenfein, Daniel Griffin, and Eran Toch, and myself.

The academic funding scandals plaguing 2019 have highlighted some of the more problematic dynamics between tech industry money and academia (see e.g. Williams 2019, Orlowski 2017). But the tech industry’s deeper impacts on academia and knowledge production actually stem from the entirely non-scandalous relationships between technology firms and academic institutions. Industry support heavily subsidizes academic work. That support comes in the form of direct funding for departments, centers, scholars, and events, but also through the provision of academic infrastructures like communications platforms, computational resources, and research tools. In light of the reality that infrastructures are themselves political, it is imperative to unpack the political dimensions of scholarly infrastructures provided by big technology firms, and question whether they might problematically impact knowledge production and the academic field more broadly.

Goldenfein, Benthall, Griffin, and Toch, “Private Companies and Scholarly Infrastructure – Google Scholar and Academic Autonomy”, 2019

Among other topics, the post is about how the reorientation of academia onto commercial platforms possibly threatens the autonomy that is a necessary condition of the objectivity of science (Bourdieu, 2004).

This is perhaps a cheeky argument. Questioning whether Big Tech companies have an undue influence on academic work is not a popular move because so much great academic work is funded by Big Tech companies.

On the other hand, calling into question the ethics of Big Tech companies is now so mainstream that it is actively debated in the Democratic 2020 primary by front-running candidates. So we are well within the Overton window here.

On a philosophical level (which is not the primary orientation of the joint work), I wonder how much these concerns are about the relationship between capitalist modes of production and ideology with academic scholarship in general, and how much this specific manifestation (Google Scholar’s becoming the site of a disciplinary collapse (Benthall, 2015) in scholarly metrics is significant. Like many contemporary problems in society and technology, the “problem” may be that a technical intervention that might have at one point seemed like a desirable intervention by challengers (in the Fligstein (1997) field theory sense) is now having the political impact that is questioned and resisted by incumbents. I.e., while there has always been a critique of the system, the system has changed and so the critique comes from a different social source.


Benthall, S. (2015). Designing networked publics for communicative action. Interface, 1(1), 3.

Bourdieu, Pierre. Science of science and reflexivity. Polity, 2004.

Fligstein, Neil. “Social skill and institutional theory.” American behavioral scientist 40.4 (1997): 397-405.

Orlowski, A. (2017). Academics “funded” by Google tend not to mention it in their work. The Register, 13 July 2017.

Williams, O. (2019). How Big Tech funds the debate on AI Ethics. New Statesman America, 6 June 2019 < https://www.newstatesman.com/science-tech/technology/2019/06/how-big-tech-funds-debate-ai-ethics>.