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Sasha Luccioni: Connecting the Dots Between AI's Environmental and Social Impacts
The Gradient: Perspectives on AI
Exploring Bias in AI Models through Technical Analysis
The study dives into analyzing biases in AI models by exploring the impact of leaving parameters like gender and ethnicity open. Previous approaches of assigning labels based on classifiers have been criticized for inaccurately attributing characteristics to images that are essentially neutral. The study questioned the binary representation of gender and the common practice of using pre-trained models without scrutiny. Instead of predefined categories, the study opted for clustering images based on visual characteristics and using image-to-text models to describe images freely. The analysis revealed that different models produced varying descriptions of professions, highlighting the bias inherent in these models and the importance of choosing the right approach to mitigate bias in AI models.
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