  Measuring algorithmic bias is crucial both to assess algorithmic fairness,
and to guide the improvement of algorithms. Current methods to measure
algorithmic bias in computer vision, which are based on observational datasets,
are inadequate for this task because they conflate algorithmic bias with
dataset bias.
  To address this problem we develop an experimental method for measuring
algorithmic bias of face analysis algorithms, which manipulates directly the
attributes of interest, e.g., gender and skin tone, in order to reveal causal
links between attribute variation and performance change. Our proposed method
is based on generating synthetic ``transects'' of matched sample images that
are designed to differ along specific attributes while leaving other attributes
constant. A crucial aspect of our approach is relying on the perception of
human observers, both to guide manipulations, and to measure algorithmic bias.
  Besides allowing the measurement of algorithmic bias, synthetic transects
have other advantages with respect to observational datasets: they sample
attributes more evenly allowing for more straightforward bias analysis on
minority and intersectional groups, they enable prediction of bias in new
scenarios, they greatly reduce ethical and legal challenges, and they are
economical and fast to obtain, helping make bias testing affordable and widely
available.
  We validate our method by comparing it to a study that employs the
traditional observational method for analyzing bias in gender classification
algorithms. The two methods reach different conclusions. While the
observational method reports gender and skin color biases, the experimental
method reveals biases due to gender, hair length, age, and facial hair.
