Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation (1911.11834v2)

Abstract: Computer vision models learn to perform a task by capturing relevant statistics from training data. It has been shown that models learn spurious age, gender, and race correlations when trained for seemingly unrelated tasks like activity recognition or image captioning. Various mitigation techniques have been presented to prevent models from utilizing or learning such biases. However, there has been little systematic comparison between these techniques. We design a simple but surprisingly effective visual recognition benchmark for studying bias mitigation. Using this benchmark, we provide a thorough analysis of a wide range of techniques. We highlight the shortcomings of popular adversarial training approaches for bias mitigation, propose a simple but similarly effective alternative to the inference-time Reducing Bias Amplification method of Zhao et al., and design a domain-independent training technique that outperforms all other methods. Finally, we validate our findings on the attribute classification task in the CelebA dataset, where attribute presence is known to be correlated with the gender of people in the image, and demonstrate that the proposed technique is effective at mitigating real-world gender bias.

An Expert Analysis on Bias Mitigation in Visual Recognition

This paper from researchers at Princeton University addresses a vital issue in computer vision: mitigating bias in visual recognition models. The paper concentrates on biases such as those related to age, gender, and race, which can unintentionally inform model predictions during tasks not overtly related to these attributes, such as activity recognition or image captioning.

Methodological Contributions

The authors propose a novel benchmark known as CIFAR-10 Skewed (CIFAR-10S) for examining bias impact on model performance. This benchmark serves to artificially introduce biases in the training dataset, thus providing a controlled environment for evaluating various bias mitigation techniques. Their approach helps ensure a methodical assessment of the influence of spurious correlations on the performance of visual recognition models.

Through this benchmark, the paper presents a thorough analysis of existing bias mitigation strategies, including domain adversarial training, Reducing Bias Amplification (RBA), and domain-independent training. Notably, domain-independent training, which leverages domain-specific classifiers while sharing a generalized feature representation, is found to surpass alternative approaches in effectiveness. It effectively addresses bias by counterbalancing biased training distributions with inference techniques, yielding superior classification accuracy and reduced bias amplification.

Critique of Adversarial Training

The results expounded in the paper suggest that adversarial training, although popular for debiasing tasks, exhibits significant drawbacks. The method not only hinders the classification accuracy due to a requirement to confuse domain identification but also retains redundant encoding which undermines its effectiveness. In contrast, domain-independent approaches bypass these issues by directly considering biases and accounting for them during model training and inference.

Validation on Real-World Data

The paper extends its findings beyond synthetic datasets to real-world scenarios using the CelebA benchmark, a dataset known for gender-skewed attributes. The results here reaffirm the superiority of domain-independent approaches, which manage to strike a balance between maintaining high predictive performance and minimizing bias across sensitive attributes.

Implications and Future Directions

This body of work has profound implications for both practical applications and theoretical research in AI ethics and fairness. Practically, the proposed strategies can be integrated into existing computer vision systems to enhance their fairness, thereby making these systems viable for deployment in diverse sociocultural settings. Theoretically, these findings underscore the importance of developing bias-aware models that can operate equitably across varied domains, prompting further exploration into bias detection and mitigation in complex, real-world datasets.

Future research could extend these foundational findings by exploring continuous or non-discrete domain labels, dynamically shifting domain distributions, and implications for subsequent system operations based on recognition model outputs. Moreover, the integration of fairness criteria into training regimes could provide new avenues for improving model robustness against biases not initially identified in datasets.

In conclusion, this paper makes significant strides in understanding and mitigating bias in visual recognition systems. The methodologies and insights offered here represent critical steps in the broader effort to ensure equitable and effective AI deployment.