biaseslike
Biaseslike is a nonstandard umbrella label used in some interdisciplinary discussions to refer to biases that resemble each other across different systems—most often between humans and artificial intelligence—without implying a single underlying mechanism.
It functions as a heuristic for comparing patterns of bias rather than a formal theory. Proponents use
In practice, biaseslike might cover examples such as data-induced omissions due to unrepresentative samples, anchoring effects
In AI, it can help discuss how training data, model architectures, and objective functions can produce analogous
Critics warn that biaseslike is vague and risks obscuring important differences between cognitive biases and algorithmic
See also: cognitive bias, confirmation bias, anchoring, algorithmic bias, data bias, fairness in machine learning.