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An independent ethical assessment of an artificial intelligence system is animpartial examination of the system's development, deployment, and use inalignment with ethical values. System-level qualitative frameworks thatdescribe high-level requirements and component-level quantitative metrics thatmeasure individual ethical dimensions have been developed over the past fewyears. However, there exists a gap between the two, which hinders the executionof independent ethical assessments in practice. This study bridges this gap anddesigns a holistic independent ethical assessment process for a textclassification model with a special focus on the task of hate speech detection.The assessment is further augmented with protected attributes mining andcounterfactual-based analysis to enhance bias assessment. It covers assessmentsof technical performance, data bias, embedding bias, classification bias, andinterpretability. The proposed process is demonstrated through an assessment ofa deep hate speech detection model.

Independent Ethical Assessment of Text Classification Models: A Hate Speech Detection Case Study