It is said that the contemporary Western world has been shaped by, if not actually born from, the algorithm. We live in a computational culture – more specifically, an algorithmic culture, as Alexander Galloway (2006) pointed out more than a decade ago. One of the excellent New Economics Foundation reports puts it: ‘[Algorithms] have morphed from curating online content to curating and influencing our lives’ (McCann et al. 2018). Indeed, capitalism's current financialised mode depends entirely on algorithmic calculation, as the basis of derivatives, high-speed trading and the new fintech sector, for example. Platform capitalism relies on algorithmic machine learning and AI, as does manufacturing. Expert systems for medical diagnosis and robot surgery are built from algorithmic machine learning. Political campaigning exploits the micro-targeting of social media messages, as we have learnt from the Cambridge Analytica scandal, not to mention the Snowden revelation of the most extensive government mass surveillance operations the world has ever seen (Cadwalladr 2018).

At the same time, there is a growing critical literature on the ill effects of algorithms on our social, political and economic life, from for example Cathy O’Neil's (2016) Weapons of Math Destruction or Frank Pasquale's (2015) The Black Box Society. Also, the effects on the individual have been heavily criticised by Jaron Lanier (2011) in his You Are Not a Gadget. The so-called ‘techlash’ appears to be gathering momentum with popular TV shows such as Black Mirror. Indeed, one episode of Charlie Booker's Netflix show Nosedive has been widely touted by journalists as being prescient of China's currently in-development Social Credit System: Zhima (Sesame) Credit, to be fully rolled out by 2020, is an Ant Financial product from the giant Alibaba online retail corporation. As Ed Jefferson (2018) has noted, while in Nosedive the social media rating was shaped by other people, in China it is the state or corporate entities that determine your credit status. This would be considered by many as the tyranny of the algorithm (Harris 2018).

In this quite broad context, the questions entertained here are comparatively limited.