Data-centric AI encourages the need of cleaning and understanding of data in
order to achieve trustworthy AI. Existing technologies, such as AutoML, make it
easier to design and train models automatically, but there is a lack of a
similar level of capabilities to extract data-centric insights. Manual
stratification of tabular data per a feature (e.g., gender) is limited to scale
up for higher feature dimension, which could be addressed using automatic
discovery of divergent subgroups. Nonetheless, these automatic discovery
techniques often search across potentially exponential combinations of features
that could be simplified using a preceding feature selection step. Existing
feature selection techniques for tabular data often involve fitting a
particular model in order to select important features. However, such
model-based selection is prone to model-bias and spurious correlations in
addition to requiring extra resource to design, fine-tune and train a model. In
this paper, we propose a model-free and sparsity-based automatic feature
selection (SAFS) framework to facilitate automatic discovery of divergent
subgroups. Different from filter-based selection techniques, we exploit the
sparsity of objective measures among feature values to rank and select
features. We validated SAFS across two publicly available datasets (MIMIC-III
and Allstate Claims) and compared it with six existing feature selection
methods. SAFS achieves a reduction of feature selection time by a factor of 81x
and 104x, averaged cross the existing methods in the MIMIC-III and Claims
datasets respectively. SAFS-selected features are also shown to achieve
competitive detection performance, e.g., 18.3% of features selected by SAFS in
the Claims dataset detected divergent samples similar to those detected by
using the whole features with a Jaccard similarity of 0.95 but with a 16x
reduction in detection time.

该论文提出了一个面向数据的 AI 框架，可以自动选择重要特征，实现自动发现不同的数据子组并提高检测性能。通过在 MIMIC-III 和 Allstate Claims 公开数据集上的验证，该框架比现有的六种特征选择方法减少了 81 倍和 104 倍的特征选择时间，并实现了有竞争力的检测性能。