Modern AI systems have made it easy to tackle many problems previously thought out of reach of computers. You have possibly heard of some of these successes such as:
These systems are so good that they have convinced even those working on developing them that they are sentient.
However, despite the successes, many of these systems can be thought of as technological parrots. Parrots can mimic their owners, but do not have a true awareness of what they are saying, nor why they are saying it.
Similarly, modern AI systems can mimic the patterns they have learnt from previous data, without having the true context of the problem which is being solved, nor understanding why a given prediction is returned. Modern AI systems are parrots at both massive scale, GPT-3 was trained on approximately 3 billion web pages, and with huge societal implications.
The end result of this parroting is that modern AI systems suffer from the following three B’s:
These three B’s mean that modern AI systems are flawed at tackling the nuanced, complex and high risk applications which they are being applied to. Let’s explore how a causal AI approach can help.
Modern AI systems are blind to the type of relationship between data points and lack context on the problems which they are being used to solve.
To illustrate this consider the relationship between years of experience and income. Typically, someone’s experience is correlated with their income: more experience trends with a higher salary. This is also true in reverse: a higher income trends with more experience. You can call this two-way correlation an associational relationship.
Figure 1: Graph showing the positive correlation between years of experience and income.
The other type of relationship is a causal one. In this case, one variable causes the change in another. The income someone earns is because of their years of experience*. Unlike the associational relationship, causality is one-way; an individual’s experience isn’t caused by the income they earn.
Causal techniques provide you with the tools to separate association from causation. By intervening on the system and setting someone’s experience to a given value you can observe how this would change their income. Using interventions you can determine the type of relationship between experience and income (causal or association), and in which direction it flows (experience causes income). You can think of interventions as a way of answering certain types of “what if” questions: What if I was 45, instead of 31, how much would I earn?