References

Towards a rigorous science of interpretable machine learning

Finale Doshi-Velez & Been Kim (2017)

arXiv preprint.

URL: https://arxiv.org/abs/1702.08608

Abstract. Doshi-Velez and Kim's foundational call for principled interpretability research — distinguishing application-, human-, and functionally-grounded evaluation of explainable AI. Predates and conceptually shapes the most-cited modern XAI methodologies.

Tags: ai-usability xai interpretability foundational

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