Automated Diversity Auditing via Face Detection Networks
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Solution Overview
Problem
Current diversity auditing systems rely on manual identification of image attributes, which does not scale well to large image sets, making them inefficient for assessing diversity in characteristics such as race, age, and gender.
Innovation Solution
A machine learning model that identifies images, detects faces, classifies them based on sensitive attributes, generates a distribution of these attributes, and computes a diversity score, allowing for automatic auditing without manual curation or tagging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of image attributes is used, then measurement precision of sensitive attributes is improved, but productivity and scalability deteriorate
Solution Approach 1:
The patent replaces the manual mechanical process of attribute identification with an automated image classification network that uses machine learning to detect and classify sensitive attributes in images. This substitution maintains measurement precision through trained algorithms while dramatically improving productivity by processing large image sets automatically without manual intervention.
Solution Approach 2:
The image classification network is trained to autonomously identify and classify sensitive attributes in images without requiring manual curation or tagging. The system serves itself by automatically learning from training data and then independently applying this knowledge to audit diversity in new image sets, eliminating the need for continuous manual identification.
2Productivity
If automated image classification is used, then productivity is improved, but device complexity increases
Solution Approach 1:
The image classification network is designed as a universal system that can handle multiple sensitive attributes (race, gender, age, etc.) simultaneously through a single unified model. This multi-functionality approach improves productivity by processing all attribute types through one system while managing complexity through shared architecture and training mechanisms rather than requiring separate systems for each attribute.
Solution Approach 2:
The system performs preliminary training of the image classification network using labeled training images before deployment. This preliminary action prepares the model in advance to automatically classify sensitive attributes, thereby improving productivity during actual auditing while the complexity is confined to the one-time training phase rather than ongoing operation.
3Measurement precision
If manual curation and tagging is performed, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent replaces the time-consuming manual curation and tagging process with an automated image classification network that rapidly processes images while maintaining measurement precision through trained algorithms. This substitution eliminates the need for manual attribute identification, dramatically reducing the time required for diversity assessment without sacrificing accuracy.
Solution Approach 2:
The automated classification system enables continuous processing of image sets without the interruptions inherent in manual workflows. The network can process images continuously in sequence, maintaining both measurement precision and high speed, thereby eliminating the time loss associated with manual curation, tagging, and data preparation steps.
Data Source
AI summary
Systems and methods for diversity auditing are described. The systems and methods include identifying a plurality of images; detecting a face in each of the plurality of images using a face detection network; classifying the face in each of the plurality of images based on a sensitive attribute using an image classification network; generating a distribution of the sensitive attribute in the plurality of images based on the classification; and computing a diversity score for the plurality of images based on the distribution.