  Complex machine learning models are deployed in several critical domains
including healthcare and autonomous vehicles nowadays, albeit as functional
black boxes. Consequently, there has been a recent surge in interpreting
decisions of such complex models in order to explain their actions to humans.
Models that correspond to human interpretation of a task are more desirable in
certain contexts and can help attribute liability, build trust, expose biases
and in turn build better models. It is, therefore, crucial to understand how
and which models conform to human understanding of tasks. In this paper, we
present a large-scale crowdsourcing study that reveals and quantifies the
dissonance between human and machine understanding, through the lens of an
image classification task. In particular, we seek to answer the following
questions: Which (well-performing) complex ML models are closer to humans in
their use of features to make accurate predictions? How does task difficulty
affect the feature selection capability of machines in comparison to humans?
Are humans consistently better at selecting features that make image
recognition more accurate? Our findings have important implications on
human-machine collaboration, considering that a long term goal in the field of
artificial intelligence is to make machines capable of learning and reasoning
like humans.
