Detecting biases in artificial intelligence has become difficult because of the impenetrable nature of deep learning. The central difficulty is in relating unobservable phenomena deep inside models with observable, outside quantities that we can measure from inputs and outputs. For example, can we detect gendered perceptions of occupations (e.g., female librarian, male electrician) using questions to and answers from a word embedding-based system? Current techniques for detecting biases are often customized for a task, dataset, or method, affecting their generalization. In this work, we draw from Psychophysics in Experimental Psychology-meant to relate quantities from the real world (i.e., “Physics”) into subjective measures in the mind (i.e., “Psyche”)-to propose an intellectually coherent and generalizable framework to detect biases in AI. Specifically, we adapt the two-alternative forced choice task (2AFC) to estimate potential biases and the strength of those biases in black-box models. We successfully reproduce previously-known biased perceptions in word embeddings and sentiment analysis predictions. We discuss how concepts in experimental psychology can be naturally applied to understanding artificial mental phenomena, and how psychophysics can form a useful methodological foundation to study fairness in AI.