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  "author": "Shobhit Agarwal",
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    {
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      "extra_author": "Yevgeniy R. Semenov",
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      "extra_author": "Yevgeniy R. Semenov",
            
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    },
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    {
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      "extra_author": "William Lotter",
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      "extra_author": "William Lotter",
            
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      "title": "Medical Imaging"
            
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  "name": "visual-classification-as-linear-combination-of-words",
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  "name": "visual-classification-as-linear-combination-of-words",
            
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  "notes": "Explainability is a longstanding challenge in deep 
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  "notes": "Explainability is a longstanding challenge in deep 
            
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learning, especially in high-stakes domains like healthcare. Common 
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learning, especially in high-stakes domains like healthcare. Common 
            
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explainability methods highlight image regions that drive an AI 
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explainability methods highlight image regions that drive an AI 
            
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model\u2019s decision. Humans, however, heavily rely on language to 
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model\u2019s decision. Humans, however, heavily rely on language to 
            
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convey explanations of not only \u201cwhere\u201d but 
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convey explanations of not only \u201cwhere\u201d but 
            
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\u201cwhat\u201d. Additionally, most explainability approaches focus 
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\u201cwhat\u201d. Additionally, most explainability approaches focus 
            
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on explaining individual AI predictions, rather than describing the 
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on explaining individual AI predictions, rather than describing the 
            
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features used by an AI model in general.",
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features used by an AI model in general.",
            
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  "title": "Visual Classification as Linear Combination of Words",
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}