Astraea: Grammar-Based Fairness Testing

Software often produces biased outputs. In particular, machine learning (ML) based software is known to produce erroneous predictions when processing
discriminatory inputs
. Such unfair program behavior can be caused by societal bias. In the last few years, Amazon, Microsoft and Google have provided software services that produce unfair outputs, mostly due to societal bias (e.g., gender or race). In such events, developers are saddled with the task of conducting
fairness testing
. Fairness testing is challenging; developers are tasked with
generating discriminatory inputs that reveal and explain biases
. We propose a
grammar-based fairness testing approach
(called
Astraea
) which leverages context-free grammars to generate discriminatory inputs that
reveal fairness violations
in software systems. Using probabilistic grammars,
Astraea
also provides fault diagnosis by
isolating the cause
of observed software bias.
Astraea
’s diagnoses facilitate the improvement of ML fairness.
Astraea
was evaluated on 18 software systems that provide three major
natural language processing
(NLP) services. In our evaluation,
Astraea
generated fairness violations at a rate of about 18%.
Astraea
generated over 573K discriminatory test cases and found over 102K fairness violations. Furthermore,
Astraea
improves software fairness by about 76% via model-retraining, on average.