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Recently, there have been breakthroughs in computer vision ("CV") models thatare more generalizable with the advent of models such as CLIP and ALIGN. Inthis paper, we analyze CLIP and highlight some of the challenges such modelspose. CLIP reduces the need for task specific training data, potentiallyopening up many niche tasks to automation. CLIP also allows its users toflexibly specify image classification classes in natural language, which wefind can shift how biases manifest. Additionally, through some preliminaryprobes we find that CLIP can inherit biases found in prior computer visionsystems. Given the wide and unpredictable domain of uses for such models, thisraises questions regarding what sufficiently safe behaviour for such systemsmay look like. These results add evidence to the growing body of work callingfor a change in the notion of a 'better' model--to move beyond simply lookingat higher accuracy at task-oriented capability evaluations, and towards abroader 'better' that takes into account deployment-critical features such asdifferent use contexts, and people who interact with the model when thinkingabout model deployment.

Evaluating CLIP: Towards Characterization of Broader Capabilities and Downstream Implications