Addressing Bias in Face Detectors using Decentralised Data collection with incentives

TL;DR

This paper proposes a decentralized data collection and labeling system with incentives to improve face detector fairness across ethnicities, genders, and ages, addressing bias issues in face detection models.

Contribution

It introduces a hybrid MultiTask Cascaded CNN with FaceNet Embeddings for bias benchmarking and a decentralized, user-involved pipeline for data enrichment and model retraining.

Findings

Bias varies across datasets and demographics

Decentralized data collection improves fairness metrics

Proposed system enhances model robustness

Abstract

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.