  Recent developments in machine learning have shown that successful models do
not rely only on huge amounts of data but the right kind of data. We show in
this paper how this data-centric approach can be facilitated in a decentralized
manner to enable efficient data collection for algorithms. Face detectors are a
class of models that suffer heavily from bias issues as they have to work on a
large variety of different data. We also propose a face detection and
anonymization approach using a hybrid MultiTask Cascaded CNN with FaceNet
Embeddings to benchmark multiple datasets to describe and evaluate the bias in
the models towards different ethnicities, gender, and age groups along with
ways to enrich fairness in a decentralized system of data labeling, correction,
and verification by users to create a robust pipeline for model retraining.
