Understanding Bias in AI
“The key to artificial intelligence has always been the representation.”
Simply put, artificial intelligence is a process involving the processing of similar data by a computer, to find trends and patterns. These patterns are used by the computer to learn or gain knowledge about a certain category. After this, algorithmic models are implemented to structure the problem-solving capacity of the computer to a desirable extent. The computer, in turn, executes this process, by giving us a simplistic output.
As children, we’re often kept away from sharp edges or hot vessels, for the sake of our safety. The simple reason is that our brains lack the cognitive development to detect certain threats and dangers. As we age, our brains start to develop, and we subconsciously keep away from possible dangers. This development is a perfect example of experience. With experience, we can gauge if something is hazardous to us (a sharp edge), and take subsequent actions (staying away).
Similarly, the data (sharp edges) that a computer comes across, helps it to react accordingly (staying away). A larger quantity of data helps build more experience and therefore increases efficiency. However, there is a vital question that we must keep in mind.
How does the computer know about how it should react to certain data?
To answer the question above, there is a very simple solution — labelling data. If we label every piece of data and the preferred reaction, the computer will begin to learn with these labels. It will then be able to understand how to react to different situations, without prior coding (as it will use its learning experience of labelled data).
Labelling data may not be as easy as it sounds. Often, we tend to make errors. Let’s take the example of an AI-based calculator. If a mathematician had to label the solution of a math equation, they would most probably get an accurate answer. However, if the same labelling task is assigned to a toddler (who has little or no experience with mathematics), the chances of an accurate answer drop significantly.
As humans, we seem to understand this relatively easily. Computers, however, only understand that there are 2 different labels for a particular solution. They have no idea which one was suggested by the toddler or the mathematician. In fact, they do not even know that a mathematician and toddler have labelled the data. Naturally, when an output is asked for, the computer will yield the mathematician’s answer 50% of the time, and the toddler’s answer 50% of the time as well.
This is not efficient at all.
If the toddler is given a chance to label another piece of data, the computer would now favor the toddler’s labelling. This would mean that the answer of the toddler would display 2 times out of every 3 times (67%), as compared to the experienced mathematician’s 1 out of 3 (33%).
