The modern train/test paradigm in Artificial Intelligence (AI) and Machine Learning (ML) narrows what we can understand about AI models, and skews our understanding of models' robustness in different environments. In this talk, I will work through the different factors involved in ethics-informed AI evaluation, including connections to ML training and ML fairness, and present an overarching evaluation protocol that addresses a multitude of considerations in developing ethical AI.
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