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The Interpretability in Deep Learning initiative is a growing team-wide effort to improve the transparency, accuracy, and fairness of AI models. AI systems and computer vision are being employed for everyday decision-making in the modern world, ranging from healthy food and clothing selections to life-altering judgments such as disease diagnosis, financial fraud detection, and employee selection. We believe it is crucial to ensure that these AI models can be interpreted by their end-users.