Been Kim

Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems

2016 · arXiv (Cornell University)

This is the Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems, held in Barcelona, Spain on December 9, 2016

Towards A Rigorous Science of Interpretable Machine Learning

2017 · arXiv (Cornell University)

As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such …

Just Say No to Single Embeddings: Why Your AI Needs Multiple Perspectives

2019 · arXiv (Cornell University)

Note: This is a work in progress document We present empirical evidence that conversations exhibit consistent geometric signatures when projected into different embedding spaces, alongside surprising variability in local feature detection. Analyzing 229 multi-agent AI …

A Benchmark for Interpretability Methods in Deep Neural Networks

2018 · arXiv (Cornell University)

We propose an empirical measure of the approximate accuracy of feature importance estimates in deep neural networks. Our results across several large-scale image classification datasets show that many popular interpretability methods produce estimates of feature …