“THERE’S NO APP FOR THINKING: AI EDITION”


Back in 2015, I wrote the article below to offer some thoughts on ‘Big Data’, which at that time was as ‘hot’ a topic as the furore which surrounds AI is today, and it can be construed as a natural progression or evolution of the ‘Big Data’ debate. The issues which were discussed then are just as pertinent to the AI debate today, and the article is therefore resubmitted in full (with AI added to all references to ‘Big Data / AI’) with a few additional observations at the end.


This article’s central theme is to ask what risks the “reification” of economics, the empirical imperative in scientific research and our increasing reliance on the genuine marvels of modern technology may pose. This is above all in terms of eroding the capacity for critical and lateral thinking, and how it might impinge on the development of social skills, particularly those related to communication, which remains the cornerstone of everyday life. The era of “Big Data / AI” is indubitably with us, and as with so much technological evolution past and present, it inevitably evokes and provokes both utopian and dystopian assessments and reactions, many of which are rooted in deep-seated, often sub- conscious, beliefs rather than in a rather more dispassionate rational analysis. As Melvin Kranzberg (1986, Technology and


Culture, 27(3): 544–560) observed: “Technology is neither good nor bad; nor is it neutral … technology’s interaction with the social ecology is such that technical developments frequently have environmental, social, and human consequences that go far beyond the immediate purposes of the technical devices and


practices themselves.”As Kranzberg’s observation underlines, ‘Big Data / AI’ is not “in and of itself” a ‘threat’. Equally, the supposition that an exponential increase in the volume of data available for research and analysis intrinsically improves our knowledge base on any given subject in terms of accuracy, objectivity and insights, which all too often are couched in linguistic terms that emphasize ‘truth’, is the stuff of modern-day myths. The term ‘Big Data / AI’ is in many ways both misleading and a mis- conceptualization. What is now being described as ‘Big Data / AI’ is often smaller than some of the ‘Big Data / AI’ which has been previously collated. For example, population census data, much of which was both cumbersome to manage and required the use of so-called “super computers” to analyse it, and was primarily of interest to social scientists. Therefore, it is important to understand that what has changed is our ability to use PCs and “off the


shelf” software packages to collect and cross- reference large quantities of often very complex data. Per se, this enables many more people to investigate, and in many cases to ‘wax lyrical’ about any number of subjects and their ostensibly associated phenomena; be they legal, psycho- social or related to physical sciences. Such a rapid and very sharp shift does find a degree of precedent in the Enlightenment era that accompanied the nascent Industrial and Scientific Revolution. Specifically, it has a profound impact on research procedures and processes, on the theory of knowledge and belief (i.e. epistemology), as well as having extraordinarily intense ethical implications. For those that espouse and embrace


empiricism, which underlines the importance of sensory experience and evidence and rigorous scientific methodology, and which also leans against explanations which rely on intuition, revelation or indeed a priori reasoning or ‘fallacies’ (as per John Stuart Mill), a world awash with ‘Big Data / AI’ would appear to be heaven-sent. This is in so far as the breadth, depth and scale of the data might appear to offer the opportunity for applied mathematics and computational


ECONOMICS... IS NOT A BODY OF CONCRETE TRUTH, BUT AN ENGINE FOR THE DISCOVERY OF TRUTH.


Alfred Marshall (1906)


26 | ADMISI - The Ghost In The Machine | Q2 Edition 2023


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33