Literature Survey: A Benchmark for Interpretability Methods in Deep Neural Networks

Thu, 06 Feb 2020 15:22:31 -0800

With the ever-increasing adoption of complex and opaque AI systems such as deep learning, explainability and interpretability in AI is becoming increasingly relevant. The lack of understanding in the decision making mechanisms of these techniques has hindered their adoption in areas where incorrect decisions are catastrophic and in heavily-audited areas (e.g. medicine and finance). Methods which shed light toward how these systems function are paramount to improving the interpretability, reliability, and auditability of these systems.

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Literature Survey: A Benchmark for Interpretability Methods in Deep Neural Networks