Aristotle’s binary classifications are now manifest throughout today’s data systems, serving, preserving, propagating, and amplifying biases up and across the machine-learning stack.
Examples of binary bias in front-end user interfaces and data processing include:
swipe right = 1, swipe left = 0
clicking “like” on Facebook = 1, not clicking like = 0
But the problem with binary logic is that it provides no scope for understanding and modeling why and howpeople have chosen one option over another. The machines are simply registering that people have made a choice, and there’s an outcome.