Shopping requires money, and in an era characterized by the expansion of both debt and speculation, financial institutions have been amongst the earliest adopters of AI. ML methods are now widely used ‘to assess credit quality, for fraud detection to price and market insurance contracts, and to automate client interaction’ (FSB 2017). But it is the highest levels of the finance sector that provide the most striking examples of AI automation. At the time of the Wall Street meltdown, stock traders were already being decimated by the algorithmic agents of high-frequency trading. Since then, stock exchanges in Tokyo, Singapore, London and Hong Kong have eliminated their trading floors. By 2018 only some 10 per cent of stock market trades were actively conducted by humans; 40 per cent are ‘passive’ trades scheduled by mutual funds; the remaining 50 per cent are executed by algorithms that are increasingly informed by ML (Pearlstein 2018; Coles 2018). In 2000, Goldman Sachs equity trading employed some 600 people: by 2016, all but two had been replaced by ‘complex trading algorithms, some with machine-learning capabilities’ (Byrnes 2017). J.P. Morgan revealed plans for an AI focus (Terekhova 2017), and poached an ML expert from Microsoft to oversee this reorganization (Kolanovic and Krishnamachari 2017). The one-time ‘masters of the universe’ were being eaten by the tools they had helped create (Byrnes 2017).
Descending from the heights of financial manipulation to the depths of everyday scraping-by, we arrive at the role of AI in social reproduction. Algorithmic profiling is ubiquitous in corporate and state decisions on loans, insurance, medical claims and job applications. It is applied with special ferocity on those who fall on the margins or outside of waged labour, i.e. the reserve army of the unemployed and the surplus populations likely to be enlarged by AI automation. As Cathy O’Neill demonstrates (2016), such machinic decisions, by intention or oversight, persistently discriminate against all indications of penury and precarity, whether by postal code, ethnicity, gender or credit history. Virginia Eubanks (2017) documents how the use of automated decision-making tools to administer welfare benefits, social housing and family care interventions constructs an AI managed ‘digital poorhouse’ that denies or deters applications, subjects recipients to police scrutiny, and stigmatizes them on the basis of behavioural patterns and past associations. A corollary of such control is the well-documented (Larson et al. 2016; Lum and Isaac 2016; Ensign et al. 2017) deployment of AIs for predictive policing and sentencing, promoted as reducing human bias, but in fact algorithmically continuing and amplifying previous patterns of judicial and police racism in a ‘runaway feedback loop’ (Reynolds 2018). This is the sharp end of the more general process by which ML, but specifically deep learning (Impett 2018), becomes in the hands of capital the means for an ever more rigorous, comprehensive and recursively self-confirming sifting, segregating and disciplining of various grades of permanent, precarious and surplus labour-power – becomes, that is, an instrument of proletarian decomposition.
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inhuman power