  Visual Question Answering systems target answering open-ended textual
questions given input images. They are a testbed for learning high-level
reasoning with a primary use in HCI, for instance assistance for the visually
impaired. Recent research has shown that state-of-the-art models tend to
produce answers exploiting biases and shortcuts in the training data, and
sometimes do not even look at the input image, instead of performing the
required reasoning steps. We present VisQA, a visual analytics tool that
explores this question of reasoning vs. bias exploitation. It exposes the key
element of state-of-the-art neural models -- attention maps in transformers.
Our working hypothesis is that reasoning steps leading to model predictions are
observable from attention distributions, which are particularly useful for
visualization. The design process of VisQA was motivated by well-known bias
examples from the fields of deep learning and vision-language reasoning and
evaluated in two ways. First, as a result of a collaboration of three fields,
machine learning, vision and language reasoning, and data analytics, the work
lead to a direct impact on the design and training of a neural model for VQA,
improving model performance as a consequence. Second, we also report on the
design of VisQA, and a goal-oriented evaluation of VisQA targeting the analysis
of a model decision process from multiple experts, providing evidence that it
makes the inner workings of models accessible to users.
