Addressing Bias in Face Detectors using Decentralised Data collection with incentives
This addresses bias issues in face detection models for users affected by unfair algorithmic outcomes, but it is incremental as it builds on existing data-centric and decentralized approaches.
The paper tackles bias in face detectors by proposing a decentralized data collection system with incentives to gather diverse data, and introduces a hybrid MultiTask Cascaded CNN with FaceNet Embeddings to benchmark and evaluate bias across ethnicities, gender, and age groups, resulting in a robust pipeline for model retraining.
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.