“Rule-based algorithms have instructions written by humans, they’re easy to comprehend. (…) Machine-learning algorithms, by contrast, have recently proved to be remarkably good at tackling problems where writing a list of instructions won’t work.”
[Hello World: Being Human in the Age of Algorithm, Hannah Fry]
Nowadays we often hear the word “algorithm” on the news and social networks. By the way, what is the algorithm? The “algorithm” is a (mathematical) recipe to accomplish a certain task. So, your grandma’s recipe for chicken soup is, in some ways, an established algorithm. But when we say about the algorithm recently, it usually refers to a computer algorithm, a series of computer languages to solve a certain problem. There are two different types of algorithms: (1) a rule-based algorithm that follows the prescribed details by humans and (2) a machine-learning algorithm that makes its own rule by machine (computer) itself.
Who does make it a rule for a new task in the future? Humans can make a crystal clear algorithm so that anybody can check the inherent bias or errors of the new rule. Machines, on the other hand, can make a high-performance algorithm without any prior knowledge and deep understanding of the new system. In the age of AI, the power of machine-learning algorithms is no way negligible and the use of this power in various fields is inevitable. However, “Great power comes great responsibility”. So, we, as humans, repeatedly scrutinize such black-box algorithms and prevent misuse of algorithms. We should always know that the final decision should come from humans because machines have no responsibility for their decision. Also, a human should provide some important rules to machine-learning algorithms such as consideration for others, tolerance, and sacrifice, which may lead to creating not only better performance algorithms but also impartial algorithms.