AI Adapts to Human Behavior Rules

The nature of machine learning operations mean they will actually deepen some of our problematic behaviors and introduce new ones of their own. This banality of machine learning is also its power.

Introduction

Artificial Intelligence (AI) is undergoing a period of massive expansion. This is not because computers have achieved human-like consciousness but because of advances in machine learning, where computers learn from training data how to classify new data. At the cutting edge are the neural networks that have learned to recognize human faces or play Go.

Recognizing patterns in data can be used as a predictive tool, and AI is being applied to echo-cardiograms to predict heart disease, to workplace data to predict if employees are going to leave and to social media feeds to detect signs of incipient depression or suicidal tendencies.

One activity that seems distant from AI is human care;

We are sure AI can sell products to our customers, but will it take care of them? Will it not exploit them to achieve it's goals? Will it know to identify their weak points and back off?

The way machine learning consumes big data and produces predictions suggests it can both grasp the enormity of the human care challenge and provide a data-driven response.

But the nature of machine learning operations mean they will actually deepen some of our bad human behaviors and introduce new ones of their own.

Thinking about how to avoid this raises wider questions about emancipator techniques and what else needs to be in place to produce machine learning for the people.

The Banality of Machine Learning is also its Power

There is no intelligence in Artificial Intelligence nor does it really learn, even though it's technical name is machine learning it is simply mathematical minimization. Like at school, fitting a straight line to a set of points you pick the line that minimizes the differences overall.

Machine learning does the same for complex patterns it fits input features to known outcomes by minimizing a cost function the fit is a model that can be applied to new data to predict the outcome. The most influential class of machine learning algorithms are neural networks which is what startups call 'deep learning'. They use back propagation: a minimization algorithm that produces weights in different layers of neurons anything that can be reduced to numbers and tagged with an outcome can be used to train a model the equations don't know or care if the numbers represent Amazon sales or earthquake victims.

This banality of machine learning is also its power. It's a generalized numerical compression of questions that matter, there is no comprehension within the computation. the patterns are correlation not causation, the only intelligence comes in the same sense as military intelligence; that is, targeting.

But models produced by machine learning can be hard to reverse into human reasoning. Why did it pick this person as a bad parole risk? what does that pattern of weights in the 3rd layer represent? we can't necessarily say.

Reasoning & the Nature of Decision Making

Machine learning doesn't just make decisions without giving reasons, it modifies our very idea of reason, that is, it changes what is knowable and what is understood as real. It operationalises the two-world metaphysics of neoplatonism that behind the world of the sensible is the world of the form or the idea. A belief in a hidden layer of reality which is ontologically superior, expressed mathematically and apprehended by going against direct experience.

Machine learning is not just a method but a machinist philosophy. What might this mean for the why we are used to see computing results? The production of opaque predictions with calculative authority will deepen the self-referential nature of decision making while providing a gloss of grounded and testable interventions.

Testing against unused data will produce hard numbers for accuracy and error while making the reasoning behind them inaccessible to debate or questioning. Using neural networks will align with the output driven focus of the logframe while deepening the disconnect between outputs and wider values.

Hannah Arendt said many years ago that cycles of social reproduction have the character of automatism. The general threat of AI, is not the substitution of humans by machines but the computational extension of existing social automatism.

Avoid from Propagate Discrimination

Of course the every field and sector are not naive about the perils of datafication. We all know machine learning could propagate discrimination because it learns from social data. Implementing Machine Learning and AI solutions we must be very careful to ensure all possible safeguards against biased training data, but the deeper effect of machine learning is to produce new subjects and to act on them.

Machine learning is performative, in the sense that reiterative statements produce the phenomena they regulate. AI will optimize the impact of limited resources applied to nearly limitless need by constructing populations that fit the needs of our organisations. The foreclosure of futures on the basis of correlation rather than causation it constructs risk in the same way that twitter determines trending topics the result will be algorithmic states of exception.

According to Agamben, the signature of a state of exception is ‘force-of’ actions that have the force of law even when not of the law. Logistic regression and neural networks generate mathematical boundaries but cybernetic exclusions will have effective force by allocating and withholding resources a process that can't be humanized by having a human-in-the-loop because it is already a technique, a co-constituting of the human and the technical.

Summery

The impact of machine learning is contingent and can be changed. It's not a question of people versus machines but of a human techniques of mutual aid. The prerequisites will be to have a standpoint, to be situated, and to be committed. If an alternative techniques is not mobilized, the next generation of scandals will be driven by AI.