Shared Latent Space Debiasing for Fair ML Without Demographics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning systems struggle to achieve fairness without access to demographic data due to privacy concerns and legal constraints, leading to biased outcomes that fail to satisfy parity-based group fairness standards and often misalign with intended protected attributes.
Innovation Solution
A shared latent space-based debiasing technique that utilizes a target encoder, source encoder, protected group estimator model, and adversarial network to generate protected features and debias classifier models, optimizing for canonical correlation and cross-entropy losses to align with parity-based fairness standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning systems are trained on historical data without demographic data to protect privacy and comply with legal constraints, then privacy protection and legal compliance are improved, but fairness and bias reduction deteriorate
Solution Approach 1:
The patent introduces protected group estimates as an intermediary construct that enables fairness intervention without requiring direct access to protected attributes. The encoder generates latent representations that capture group information indirectly, allowing the system to address bias while maintaining privacy compliance. This mediator layer bridges the gap between privacy protection and fairness achievement.
Solution Approach 2:
The system performs preliminary encoding to generate protected group estimates before the main classification task. By pre-processing the data to create latent representations of protected groups, the system prepares the necessary information for fairness intervention in advance, enabling subsequent debiasing operations without requiring actual demographic data during training or deployment.
2Productivity
If existing fair algorithms without demographics are used to maximize utility for disadvantaged groups, then representation bias is reduced, but parity-based group fairness standards are not satisfied and alignment with intended protected attributes deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the generated protected group estimates are used to guide the training process. The loss function incorporates fairness constraints that provide feedback signals to adjust model parameters, ensuring that the system learns to produce outcomes that satisfy parity-based fairness standards while maintaining alignment with the intended protected attributes.
Solution Approach 2:
The system changes the parameter space by working with latent representations rather than raw demographic data. By transforming the problem into the latent space where protected group information is encoded indirectly, the system can manipulate fairness-related parameters without directly accessing sensitive attributes, thus achieving both utility improvement and precise alignment with protected groups.
Data Source
AI summary
Methods, systems, and computer program products are provided for shared latent space-based debiasing. An example system includes at least one processor configured to: transform data from each of a target domain, which lacks protected features, and a separate source domain, which contains these features, into correlated latent representations; jointly train a cross-domain protected group estimator on the representations; and debias a downstream machine learning model an adversarial learning technique that leverages the group estimator.