Comparing Humans and Models on a Similar Scale: Towards Cognitive Gender
  Bias Evaluation in Coreference Resolution
Gili Lior Gabriel Stanovsky
Spurious correlations were found to be an important factor explaining model
performance in various NLP tasks (e.g., gender or racial artifacts), often
considered to be ''shortcuts'' to the actual task. However, humans tend to
similarly make quick (and sometimes wrong) predictions based on societal and
cognitive presuppositions. In this work we address the question: can we
quantify the extent to which model biases reflect human behaviour? Answering
this question will help shed light on model performance and provide meaningful
comparisons against humans. We approach this question through the lens of the
dual-process theory for human decision-making. This theory differentiates
between an automatic unconscious (and sometimes biased) ''fast system'' and a
''slow system'', which when triggered may revisit earlier automatic reactions.
We make several observations from two crowdsourcing experiments of gender bias
in coreference resolution, using self-paced reading to study the ''fast''
system, and question answering to study the ''slow'' system under a constrained
time setting. On real-world data humans make $\sim$3\% more gender-biased
decisions compared to models, while on synthetic data models are $\sim$12\%
more biased.