Senior Research Fellow John Reader questions the power of the algorithm in this review of Ed Finn’s book ‘What Algorithms Want’.
This book sets out to examine how computational systems are transforming the world in which we live and work, and to develop an “algorithmic” interpretation of the world (p. 5).
One means of doing so can be derived from the work of Ian Bogost who talks about our increasingly mythological relationship with software, in which we replace God with the algorithm and religious buildings with the “Cathedral of Computation”. A faith-based relationship with the algorithm is now trusted to guide us through the city streets, for instance, and indeed to answer most of the questions we have about life in general. Although we imagine that such algorithms are elegant, simple and efficient, they are actually sprawling assemblages involving many forms of human labour, material resources and ideological choices (p. 7). They may be perceived or presented as the pinnacle of Enlightenment rationalism, but our engagements with them function in a very different mode. The cathedral metaphor suggests that there is an ordering logic, superstructure or ontology for how we organise meaning in our lives—effectively a universal system of knowledge. In which case, the book argues, this is a significant and philosophically inflected development that demands analysis.
Chapter One sets the scene by investigating algorithms themselves. Algorithms are recipes, instruction sets, or a sequence of tasks designed to achieve a particular calculation or result (p. 17). The current focus of research is not so much how they work but rather about how efficiently they function. Most of today’s powerful technology companies such as Google, Amazon and Facebook are essentially “cultural wrappers for sophisticated algorithms” (p. 20). However, these algorithms have changed our world, and the belief that they are vehicles for unbiased decision-making is misplaced. Instead, what we are dealing with is “effective computability” and crucial for this process is the practice of abstraction. Yet every such abstraction has a remainder: that which is excluded in order to formulate a neat answer. According to Finn, the most important abstraction of all is the desire for an answer, and therein lies both the power and the fascination of the algorithm in contemporary society.
Gradually, we are beginning to grasp that the technology now makes decisions on our behalf, presaging the point at which it transcends humanity in its capabilities and range. This takes us, however, into the issue of implementation. Implementation presupposes both the capacity of the technology to shape our experience of the world and the human capacity to engage imaginatively with the technology as we develop it:
“implementation runs both ways—every culture machine we build to interface with the embodied world of human materiality also reconfigures that embodied space, altering cognitive and cultural practices” (p. 49).
Our desire for universal knowledge draws us into the power of the abstractions which determine the algorithms, but then runs the risk of excluding those other elements which don’t fit neatly into their efficient functioning.
Finn begins his next chapter with a description of how Apple’s virtual assistant Siri was developed and how it is designed to function:
“like so many other big data, algorithmic machines, it depends on a deep well, a cistern of human attention and input that serves as an informational reservoir for computational inference” (p. 62).
It navigates the world through ideas rather than syntax, but it is a station on the path to the quest for perfect self-knowledge. Anticipation is the key term in this discussion: the technology will tell you what you want before you have even worked that out for yourself. On the basis of its past experience of your searches it can predict and anticipate your next moves and desires. As Finn says:
“the Google culture machine is assembling a map that at times threatens to upstage the territory” (p. 74).
Chapter Five takes us into the realm of Bitcoin as an example of algorithmic arbitrage, this time in the field of finance and high-speed trading. Trust is being constructed by and through the technology but in a way that is very different from previous means of creating such relationships. The navigation and manipulation of the data become more important than the data itself, just as the functioning of whichever interface becomes more important than any relationship which might lie behind it. What are the implications of this for the public sphere and public life more generally? Now that the current pandemic crisis has encouraged more of us to resort to Skype and Zoom in order to be in contact with others both professionally and domestically, what is lost and what is gained in the process, and how much of this practice will be sustained once a different normality is resumed? How might this impact upon our understandings of democracy and ways in which this could now function?
Finn’s final chapter turns to the concept of algorithmic imagination. Can there be such a thing, and if so what would it look like and how would it differ from what we understand as normal human imagination? This depends partly upon how the technology develops, but also upon our own expectations of what works in human relationships. Could there be an augmented imagination bringing the human and the digital into ever closer assemblages? Finn remains essentially optimistic despite all his misgivings and argues for what he calls an “Experimental Humanities”—a discipline which requires a greater transparency into how algorithms are devised and deployed and a deeper intellectual engagement with the processes involved. Both of these principles seem vital if the technology is not to outrun the human capacity to exercise critical thought and to guard against the worst impacts of commercial manipulation.
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