What are examples of computer vision bugs related to race?

Computer vision systems can exhibit bugs related to race due to biases in training data, algorithmic design, or testing practices. These issues often result in lower accuracy or harmful misclassifications for people of certain racial groups. Here are three key examples:

1. Facial Recognition Inaccuracies Facial recognition systems have historically shown higher error rates for people with darker skin tones. For instance, a 2018 MIT study found that commercial facial analysis tools from companies like Microsoft and IBM had error rates of up to 34.7% for darker-skinned women, compared to 0.8% for lighter-skinned men. This disparity stems from training datasets that disproportionately include lighter-skinned individuals, causing the models to learn features biased toward those groups. Developers might not intentionally exclude diverse data, but without deliberate effort to include balanced representation, the models fail to generalize. Such flaws can lead to real-world harms, like misidentification in security systems or denied access to services.

2. Image Classification Errors Image classification models sometimes mislabel individuals based on race. A notorious example occurred when Google Photos’ algorithm classified images of Black people as “gorillas” in 2015. This error arose because the training data lacked sufficient examples of darker skin tones, causing the model to map features incorrectly. Similarly, auto-white-balance features in cameras have struggled to adjust for darker skin, producing overexposed or underexposed photos. These issues highlight how neglecting diverse data during preprocessing or model training can lead to exclusionary outcomes. Developers often overlook edge cases during testing, assuming uniform performance across demographics, which reinforces systemic biases.

3. Biases in Surveillance and Object Detection Surveillance systems using computer vision have shown racial bias in object detection. For example, Amazon’s Rekognition tool once incorrectly matched 28 members of the U.S. Congress (disproportionately people of color) to criminal mugshots due to higher false-positive rates for non-white faces. Similarly, emotion recognition systems often misinterpret expressions like anger or neutrality across racial groups, as they’re trained on datasets dominated by Western facial expressions. These bugs occur because models are optimized for majority demographics, and validation metrics (e.g., overall accuracy) mask subgroup failures. Developers must prioritize disaggregated testing by race to surface and address these gaps.

In summary, addressing racial biases in computer vision requires intentional dataset curation, rigorous subgroup testing, and transparency in model limitations. Developers play a critical role in mitigating these issues through better data practices and algorithmic fairness checks.