The mythos of model interpretability

Supervised machine-learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world?

[1]  Illtyd Trethowan Causality , 1938 .

[2]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[3]  Thomas Richardson,et al.  Interpretable Boosted Naïve Bayes Classification , 1998, KDD.

[4]  John David N. Dionisio,et al.  Case-based explanation of non-case-based learning methods , 1999, AMIA.

[5]  B. Winblad,et al.  Smoking and the Occurence of Alzheimer's Disease: Cross-Sectional and Longitudinal Data in a Population-based Study , 1999 .

[6]  B. Winblad,et al.  Smoking and the occurrence of Alzheimer's disease: cross-sectional and longitudinal data in a population-based study. , 1999, American journal of epidemiology.

[7]  Changchun Liu,et al.  An empirical study of machine learning techniques for affect recognition in human-robot interaction , 2005 .

[8]  Changchun Liu,et al.  An empirical study of machine learning techniques for affect recognition in human–robot interaction , 2006, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[10]  Chong Wang,et al.  Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.

[11]  Bart Baesens,et al.  An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models , 2011, Decis. Support Syst..

[12]  Johannes Gehrke,et al.  Intelligible models for classification and regression , 2012, KDD.

[13]  Siddhartha S. Srinivasa,et al.  Legibility and predictability of robot motion , 2013, 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[14]  Johannes Gehrke,et al.  Accurate intelligible models with pairwise interactions , 2013, KDD.

[15]  Jure Leskovec,et al.  Hidden factors and hidden topics: understanding rating dimensions with review text , 2013, RecSys.

[16]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[17]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[18]  Joan Bruna,et al.  Intriguing properties of neural networks , 2013, ICLR.

[19]  Andrew Zisserman,et al.  Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.

[20]  Cynthia Rudin,et al.  The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification , 2014, NIPS.

[21]  Andrea Vedaldi,et al.  Understanding deep image representations by inverting them , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  G. Imbens,et al.  Machine Learning Methods for Estimating Heterogeneous Causal Eects , 2015 .

[23]  Alexander Mordvintsev,et al.  Inceptionism: Going Deeper into Neural Networks , 2015 .

[24]  Been Kim,et al.  iBCM: Interactive Bayesian Case Model Empowering Humans via Intuitive Interaction , 2015 .

[25]  Johannes Gehrke,et al.  Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission , 2015, KDD.

[26]  Ryan P. Adams,et al.  Graph-Sparse LDA: A Topic Model with Structured Sparsity , 2014, AAAI.

[27]  Been Kim,et al.  Interactive and interpretable machine learning models for human machine collaboration , 2015 .

[28]  David C. Kale,et al.  Modeling Missing Data in Clinical Time Series with RNNs , 2016 .

[29]  Tom Schaul,et al.  Dueling Network Architectures for Deep Reinforcement Learning , 2015, ICML.

[30]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[31]  Alexandra Chouldechova,et al.  Fair prediction with disparate impact: A study of bias in recidivism prediction instruments , 2016, Big Data.

[32]  Karen M. Feigh,et al.  Learning From Explanations Using Sentiment and Advice in RL , 2017, IEEE Transactions on Cognitive and Developmental Systems.

[33]  Seth Flaxman,et al.  European Union Regulations on Algorithmic Decision-Making and a "Right to Explanation" , 2016, AI Mag..

[34]  Zachary C. Lipton,et al.  The mythos of model interpretability , 2018, Commun. ACM.