- Anti-surveillance movements. Since Edward Snowden’s revelations, awareness of the scope of surveillance in capitalist liberal democracies has escalated (Gallagher 2018) (though of course such surveillance is no news to populations elsewhere – for example in China). This is an AI issue, not just because the NSA uses ML in its scans, but because surveillance is crucial in the development of AI. As we have described, large data sets are necessary to train ML systems. Not all of these data sets necessarily scrutinize human behaviour, and there are some technica attempts to mitigate the invasiveness of such observation (Knight 2016b; Marr 2018), but AI development is likely to remain significantly reliant on large-scale people watching. In North America, anti-surveillance self-defence manuals and information sheets have proliferated across social movements. ‘User’s guides for privacy and protest’ summarize practices of ‘obfuscation – the deliberate addition of ambiguous, confusing, or misleading information to interfere with surveillance and data collection’ (Brunton and Nissenbaum 2015). Facial recognition AI, and the possibility of baffling it with ‘adversarial images’, is a recent hot-spot. There are, however, serious limitations of time, expense and expertise on individualized anti-surveillance techno-measures. Politica and legal limits to surveillance have better chances of mass effect. In North America, some of the most important initiatives are from minorities that know only too well how profiling systems at once constitute and control suspect social groups (Dyer-Witheford and Matviyenko 2019): African Americans, opposing the digital tracking both of entire neighbourhoods and Black Lives Matter leaders; First Nations fighting the digita scrutiny of land and pipeline protests; and Muslim Arabs, opposing wholesale databank inventories of their existence. The major politica steps in surveillance restriction have, however, come from Europe, where memories of Nazism and Stalinism are still alive. Anti-corporate surveillance movements such as Max Schrem’s ‘None of Your Business’, and governing elites’ opposition to national sovereignty infringements give the issue real political weight. The European Union’s General Data Protection Regulation (GDPR), which came into force in 2018, limits the data gathering of digital capital, including the great AI developers. Its requirements for active user opt-in (rather than default acceptance) of digital tracking, corporate transparency about the use of personal data, users’ right to leave platforms without losing stored data, explanations of automated decisions, and for a ‘right to be forgotten’ all crimp the use of population data as extractive raw material for the construction of machine intelligence (Meyer 2018; Kaput 2018; Srnicek 2018). The negative response of ML developers and computer scientists, and their critical comparison of Europe’s regulations with the corporate freedoms of the United States and the powers of China’s surveillance state (K-F. Lee 2018), indicate how big a problem anti-surveillance movements pose to AI-capital, but also the challenges such movements face in an AI-competitive world-market.
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inhuman power