PATTERN PLEASES US, REWARDS A MIND SEDUCED AND YET EXHAUSTED BY COMPLEXITY.


Diane Ackerman


would be in for a serious disappointment. Indeed, it might well happen that however long he searched he would not find a single pebble weighing exactly 145 grams. The statistical method shows the facts in the light of the ideal average but does not give us a picture of their empirical reality. While reflecting an indisputable aspect of reality, it can falsify the actual truth in a most misleading way.” (The Undiscovered Self, 1958). In a similar vein, a further aspect that needs to be borne in mind is that some statistical relationships are in fact casual or coincidental, rather than causal, above all in respect to the phenomenon of ‘apophenia’ (also known as ‘patternicity’), which refers to the human tendency to seek patterns in random information. It highlights a confluence of behavioural tendencies, which Diane Ackerman (An Alchemy of Mind: The Marvel and Mystery of the Brain, 2004) summarized succinctly: “Pattern pleases us, rewards a mind seduced and yet exhausted by complexity. We crave pattern, and find it all around us, in petals, sand dunes, pine cones, contrails. Our buildings, our symphonies, our clothing, our societies - all declare patterns. Even our actions: habits, rules, codes of honour, sports, traditions - we have many names for patterns of conduct. They reassure us that


life is orderly.” Therefore ‘Big Data / AI’ certainly can be


said to play into an instinctive form of human behaviour, but such pattern-seeking carries with it very substantial ethical considerations in the social sphere, in the very broadest sense. This is not just a matter of data being collected and mined from sources (e.g. social media such as Facebook or Twitter) where the user is effectively


unaware, and has in many cases not consented to it, despite effectively operating in a supposedly ‘public space’, which may legally be seen as a form of tacit consent, but is far from it in ethical terms. More importantly, without due process, it is likely to create differentiated access to any given data set, in other words inequality, which can in turn lead to distortions to how we interact with the data. As but one example, if researcher X is given access to a set of data, but avoids investigating a putative phenomenon or positing certain hypotheses which might jeopardize access to data, by dint of being contentious to the data provider, this imparts an asymmetric skew to research, which goes against the very principles of empiricism. As Jacques Derrida (Archive Fever: A Freudian Impression, 1996) suggested in relationship to archives, the ‘effective democratisation’ of ‘Big Data / AI’ can be measured very simply by ‘the participation in and access to the archive, its constitution, and its interpretation’. AI is far more than the capacity to process


big data, but for the foreseeable future, it must be borne in mind that Machine Learning and Deep Learning are governed by hard logic, and that AI is not by any means infallible. By contrast human decision making does involve an element of logic, but is also governed by emotion and intuition, the latter effectively being the sum of a person’s experiences, and theretofore associated feelings, generated by our senses. This what makes human beings ‘sentient’. There is little doubt that AI has the power to free us from many of the mundane tasks, thus streamlining many activities, and by extension enhancing amongst other things workplace productivity, and very notably in such areas as education and healthcare, as well as the numerous challenges associated with Climate Change and energy transition. It should however be remembered that a good deal of the ‘big data’ on which AI relies is drawn from the internet, which is anything but an unadulterated body of truth. But these various issues and observations


should not be construed as a barrier to ‘Big Data / AI’. The more immediate issues are perhaps far more fundamental. Firstly, ‘Big Data / AI’ is in truth a very new phenomenon, per se there is an inherent “skills shortage” and “skills gap” in the workforce,


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


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