  Neural language models have achieved human level performance across several
NLP datasets. However, recent studies have shown that these models are not
truly learning the desired task; rather, their high performance is attributed
to overfitting using spurious biases, which suggests that the capabilities of
AI systems have been over-estimated. We introduce a generic formula for Data
Quality Index (DQI) to help dataset creators create datasets free of such
unwanted biases. We evaluate this formula using a recently proposed approach
for adversarial filtering, AFLite. We propose a new data creation paradigm
using DQI to create higher quality data. The data creation paradigm consists of
several data visualizations to help data creators (i) understand the quality of
data and (ii) visualize the impact of the created data instance on the overall
quality. It also has a couple of automation methods to (i) assist data creators
and (ii) make the model more robust to adversarial attacks. We use DQI along
with these automation methods to renovate biased examples in SNLI. We show that
models trained on the renovated SNLI dataset generalize better to out of
distribution tasks. Renovation results in reduced model performance, exposing a
large gap with respect to human performance. DQI systematically helps in
creating harder benchmarks using active learning. Our work takes the process of
dynamic dataset creation forward, wherein datasets evolve together with the
evolving state of the art, therefore serving as a means of benchmarking the
true progress of AI.
