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Recent developments in machine learning have shown that successful models donot rely only on huge amounts of data but the right kind of data. We show inthis paper how this data-centric approach can be facilitated in a decentralizedmanner to enable efficient data collection for algorithms. Face detectors are aclass of models that suffer heavily from bias issues as they have to work on alarge variety of different data. We also propose a face detection andanonymization approach using a hybrid MultiTask Cascaded CNN with FaceNetEmbeddings to benchmark multiple datasets to describe and evaluate the bias inthe models towards different ethnicities, gender, and age groups along withways to enrich fairness in a decentralized system of data labeling, correction,and verification by users to create a robust pipeline for model retraining.

Addressing Bias in Face Detectors using Decentralised Data collection with incentives