Does Object Recognition Work for Everyone?
This highlights a fairness issue in AI for global users, showing current systems are biased and not inclusive across different regions and income levels.
The paper analyzed object-recognition systems on a geographically diverse dataset and found they perform poorly on household items from low-income countries, with accuracy drops due to appearance differences and contextual variations.
The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset. This dataset contains household items and was designed to have a more representative geographical coverage than commonly used image datasets in object recognition. We find that the systems perform relatively poorly on household items that commonly occur in countries with a low household income. Qualitative analyses suggest the drop in performance is primarily due to appearance differences within an object class (e.g., dish soap) and due to items appearing in a different context (e.g., toothbrushes appearing outside of bathrooms). The results of our study suggest that further work is needed to make object-recognition systems work equally well for people across different countries and income levels.