Tag Archives: Facial recognition

Bridle’s vision

Belatedly finished reading James Bridle’s book New Dark Age: Technology and the End of the Future (Verso, 2018). As the title suggests, the text is systemically pessimist about the effect of new technologies on the sustainability of human wellbeing. Although the overall structure of the argument is at times clouded over by sudden twists in narrative and the sheer variety of anecdotes, there are many hidden gems. I very much enjoyed the idea, borrowed from Timothy Morton, of a hyperobject:

a thing that surrounds us, envelops and entangles us, but that is literally too big to see in its entirety. Mostly, we perceive hyperobjects through their influence on other things […] Because they are so close and yet so hard to see, they defy our ability to describe them rationally, and to master or overcome them in any traditional sense. Climate change is a hyperobject, but so is nuclear radiation, evolution, and the internet.

One of the main characteristics of hyperobjects is that we only ever perceive their imprints on other things, and thus to model the hyperobject requires vast amounts of computation. It can only be appreciated at the network level, made sensible through vast distributed systems of sensors, exabytes of data and computation, performed in time as well as space. Scientific record keeping thus becomes a form of extrasensory perception: a networked, communal, time-travelling knowledge making. (73)

Bridle has some thought-provoking ideas about possible responses to the dehumanizing forces of automation and algorithmic sorting, as well. Particularly captivating was his description of Gary Kasparov’s reaction to defeat at the hands of AI Deep Blue in 1997: the grandmaster proposed ‘Advanced Chess’ tournaments, pitting pairs of human and computer players, since such a pairing is superior to both human and machine players on their own. This type of ‘centaur strategy’ is not simply a winning one: it may, Bridle suggests, hold ethical insights on patways of human adaptation to an era of ubiquitous computation.

Coded Bias

I managed to catch a screening of the new Shalini Kantayya documentary, Coded Bias, through EDRi. It tells the story of Joy Bualomwini‘s discovery of systematic discrepancies in the performance of algorithms across races and genders. The tone was lively and accessible, with a good tempo, and the cast of characters presented did a good job showcasing a cross-section of female voices in the tech policy space. It was particularly good to see several authors that appear on my syllabus, such as Cathy O’Neil, Zeynep Tufekci, and Virginia Eubanks.

Sharp Eyes

An interesting report in Medium (via /.) discusses the PRC’s new pervasive surveillance program, Sharp Eyes. The program, which complements several other mass surveillance initiatives by the Chinese government, such as SkyNet, is aimed especially at rural communities and small towns. With all the caveats related to the fragmentary nature of the information available to outside researchers, it appears that Sharp Eyes’ main characteristic is being community-driven: the feeds from CCTV cameras monitoring public spaces are made accessible to individuals in the community, whether at home from their TVs and monitors or through smartphone apps. Hence, local communities become responsible for monitoring themselves (and providing denunciations of deviants to the authorities).

This outsourcing of social control is clearly a labor-saving initiative, which itself ties in to a long-run, classic theme in Chinese governance. It is not hard to perceive how such a scheme may encourage social homogeneization and irregimentation dynamics, and be especially effective against stigmatized minorities. After all, the entire system of Chinese official surveillance is more or less formally linked to the controversial Social Credit System, a scoring of the population for ideological and financial conformity.

However, I wonder whether a community-driven surveillance program, in rendering society more transparent to itself, does not also potentially offer accountability tools to civil society vis-à-vis the government. After all, complete visibility of public space by all members of society also can mean exposure and documentation of specific public instances of abuse of authority, such as police brutality. Such cases could of course be blacked out of the feeds, but such a heavy-handed tactic would cut into the propaganda value of the transparency initiative and affect public trust in the system. Alternatively, offending material could be removed more seamlessly through deep fake interventions, but the resources necessary for such a level of tampering, including the additional layer of bureaucracy needed to curate live feeds, would seem ultimately self-defeating in terms of the cost-cutting rationale.

In any case, including the monitored public within the monitoring loop (and emphasizing the collective responsibility aspect of the practice over the atomizing, pervasive-suspicion one) promises to create novel practical and theoretical challenges for mass surveillance.

Behavioral redefinition

Vice reports on a Tokyo-based company, DeepScore, pitching software for the automatic recognition of ‘trustworthiness’, e.g. in loan applicants. Although their claimed false-negative rate of 30% may not sound particularly impressive, it must of course be compared to well-known human biases in lending decisions. Perhaps more interesting is the instrumentalization cycle, which is all but assured to take place if DeepScore’s algorithm gains wide acceptance. On the one hand, the algorithm’s goal is to create a precise definition for a broad and vague human characteristic like trustworthiness—that is to say, to operationalize it. Then, if the algorithm is successful on its training sample and becomes adopted by real-world decision-makers, the social power of the adopters reifies the research hypothesis: trustworthiness becomes what the algorithm says it is (because money talks). Thus, the behavioral redefinition of a folk psychology concept comes to fruition. On the other hand, however, instrumentalization immediately kicks in, as users attempt to game the operationalized definition, by managing to present the algorithmically-approved symptoms without the underlying condition (sincerity). Hence, the signal loses strength, and the cycle completes. The fact that DeepScore’s trustworthiness algorithm is intended for credit markets in South-East Asia, where there exist populations without access to traditional credit-scoring channels, merely clarifies the ‘predatory inclusion’ logic of such practices (v. supra).


Given the recent salience of news on surveillance and surveillance capitalism, it is to be expected that there would be rising interest in material, technical countermeasures. Indeed, a cottage industry of surveillance-avoidance gear and gadgetry has sprung up. The reviews of these apparatuses tend to agree that the results they achieve are not great. For one thing, they are typically targeted at one type of surveillance vector at a time, thus requiring a specifically tailored attack model rather than being comprehensive solutions. Moreover, they can really only be fine-tuned properly if they have access to the source code of the algorithm they are trying to beat, or at least can test its response in controlled conditions before facing it in the wild. But of course, uncertainty about the outcomes of surveillance, or indeed about whether it is taking place to begin with, is the heart of the matter.

The creators of these countermeasures themselves, whatever their personal intellectual commitment to privacy and anonymity, hardly follow their own advice in eschewing the visibility the large internet platforms afford. Whether these systems try to beat machine-learning algorithms through data poisoning or adversarial attacks, they tend to be more of a political statement and proof of concept than a workable solution, especially in the long term. In general, even when effective, using these countermeasures is seen as extremely cumbersome and self-penalizing: they can be useful in limited situations for operating in ‘stealth mode’, but cannot be lived in permanently.

If this is the technological state of play, are we destined to a future of much greater personal transparency, or is the notion of hiding undergoing an evolution? Certainly, the momentum behind the diffusion of surveillance techniques such as facial recognition appears massive worldwide. Furthermore, it is no longer merely a question of centralized state agencies: the technology is mature for individual consumers to enact private micro-surveillance. This sea change is certainly prompting shifts in acceptable social behavior. But as to the wider problem of obscurity in our social lives, the strategic response may well lie in a mixture of compartimentalization and hiding in plain sight. And of course systems of any kind are easier to beat when one can target the human agent at the other end.