Balancing Biases and Preserving Privacy on Balanced Faces in the Wild (2103.09118v5)

Abstract: There are demographic biases present in current facial recognition (FR) models. To measure these biases across different ethnic and gender subgroups, we introduce our Balanced Faces in the Wild (BFW) dataset. This dataset allows for the characterization of FR performance per subgroup. We found that relying on a single score threshold to differentiate between genuine and imposters sample pairs leads to suboptimal results. Additionally, performance within subgroups often varies significantly from the global average. Therefore, specific error rates only hold for populations that match the validation data. To mitigate imbalanced performances, we propose a novel domain adaptation learning scheme that uses facial features extracted from state-of-the-art neural networks. This scheme boosts the average performance and preserves identity information while removing demographic knowledge. Removing demographic knowledge prevents potential biases from affecting decision-making and protects privacy by eliminating demographic information. We explore the proposed method and demonstrate that subgroup classifiers can no longer learn from features projected using our domain adaptation scheme. For access to the source code and data, please visit https://github.com/visionjo/facerec-bias-bfw.

Analysis of Categorical Classification Accuracy Across Demographic Groups

The paper presents a detailed empirical paper, focusing on the classification accuracy of a statistical model across various demographic groups. The paper involves an extensive evaluation of the model’s performance, identifying significant accuracy disparities across different demographic categories. The categories examined include gender and ethnicity, with subgroups labelled as AF (African Female), AM (African Male), BF (Black Female), BM (Black Male), IF (Indian Female), IM (Indian Male), WF (White Female), and WM (White Male).

Key Findings

The primary data collected showcases two matrices of classification performance. Each matrix represents a confusion matrix where diagonal values signify accurate classification percentages, and off-diagonal values denote misclassification rates.

1. First Matrix Analysis:

The initial confusion matrix highlights a high accuracy rate along the diagonal with the following notable observations: - The classification accuracies for AF, AM, BF, BM, IF, IM, WF, and WM are 92.7%, 97.4%, 92.4%, 96.2%, 93.5%, 90.2%, 97.0%, and 84.8% respectively. - The WM category shows a noticeable drop in accuracy, indicating potential biases in the classifier for this subgroup. - Misclassification between AM and WM is considerably higher compared to other category pairings, highlighting an area for further investigation.

1. Second Matrix Analysis:

Upon comparison, the second matrix suggests a generalized reduction in classification accuracy across virtually all demographic groups: - A marked decline is seen in the WM category with an accurate classification rate of 31.7%. - IF and IM subgroups also demonstrate a drop in accuracy to 64.9% and 58.9%, respectively. - Misclassification across all non-diagonal elements has increased, pointing towards a more uniform distribution of inaccuracies across demographic lines.

Implications and Speculations

The empirical evidence suggests issues of bias and fairness within the model's framework, specifically affecting the precision across certain demographic groups. The significant disparity, particularly with the WM group, underscores potential underlying dataset imbalances or inherent biases in the classification methodology. Such performance variances necessitate both introspective dataset analysis and model reevaluation.

Practical applications of this paper's findings could lead to refined model training protocols, with a focus on achieving demographic parity and devising fairness-aware algorithms. Furthermore, the results advocate for the necessity of employing fairness metrics as integral components within model evaluation processes.

Theoretical Impact

The paper contributes to ongoing discussions concerning algorithmic fairness and bias mitigation in machine learning models. It emphasizes the importance of fairness in machine learning, particularly in high-stakes domains where inequities across demographic classifications could lead to adverse societal impacts. Future explorations should aim to develop innovative methodologies to ameliorate disparate performance outcomes across populations without sacrificing overall model efficacy.

Conclusion

This paper offers a vital observational basis for demographic classification discrepancies, encouraging the research community to explore solutions addressing these biases in AI systems. Further research is necessary to dissect these disparities and establish strategies to optimize AI models for equitable outcomes across diverse demographic groups.