Fetching the paper…
Reading the bibliography…
Recent advances in interpretable Machine Learning (iML) and eXplainable AI (XAI) construct explanations based on the importance of features in classification tasks.
Rule Extraction: Where Do We Go from Here?
Craven, M. W. and Shavlik, J. W · 1999
Earlier work this paper cites.
Extracting Decision Trees From Trained Neural Networks
Krishnan, R., Sivakumar, G., and Bhattacharya, P · 1999
Earlier work this paper cites.
An Empirical Evaluation of the Comprehensibility of Decision Table, Tree and Rule Based Predictive Models
Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., and Baesens, B · 2010
Earlier work this paper cites.
Why-Oriented End-User Debugging of Naive Bayes Text Classification
Kulesza, T., Stumpf, S., Wong, W.-K., Burnett, M. M., Perona, S., Ko, A., and Oberst, I · 2011
Earlier work this paper cites.
Principles of Explanatory Debugging to Personalize Interactive Machine Learning
Kulesza, T., Burnett, M., Wong, W.-K., and Stumpf, S · 2015
Earlier work this paper cites.
Big Data’s Disparate Impact
Barocas, S. and Selbst, A. D · 2016
Earlier work this paper cites.
Regulating by Robot: Administrative Decision Making in the Machine-Learning Era
Coglianese, C. and Lehr, D · 2016
Earlier work this paper cites.
Artificial Intelligence’s White Guy Problem
Crawford, K · 2016
Earlier work this paper cites.
Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems
Datta, A., Sen, S., and Zick, Y · 2016
Earlier work this paper cites.
Generating Visual Explanations
Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., and Darrell, T · 2016
Earlier work this paper cites.
Rationalizing Neural Predictions
Lei, T., Barzilay, R., and Jaakkola, T · 2016
Earlier work this paper cites.
The Mythos of Model Interpretability
Lipton, Z. C · 2016
Earlier work this paper cites.
An Unexpected Unity Among Methods for Interpreting Model Predictions
Lundberg, S. and Lee, S.-I · 2016
Earlier work this paper cites.
Explaining Nonlinear Classification Decisions with Deep Taylor Decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., and Müller, K. R · 2016
Cited alongside, same era.
Synthesizing the Preferred Inputs for Neurons in Neural Networks via Deep Generator Networks
Nguyen, A., Dosovitskiy, A., Yosinski, J., Brox, T., and Clune, J · 2016
Cited alongside, same era.
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Cited alongside, same era.
TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning
Thiagarajan, J. J., Kailkhura, B., Sattigeri, P., and Ramamurthy, K. N · 2016
Cited alongside, same era.
Bayesian Rule Sets for Interpretable Classification
Wang, T., Rudin, C., Velez-Doshi, F., Liu, Y., Klampfl, E., and Macneille, P · 2016
Cited alongside, same era.
Interpretable Policies for Reinforcement Learning by Genetic Programming
Hein, D, Udluft, S, and Runkler, T. A · 2017
Later among the works it cites.
The Promise and Peril of Human Evaluation for Model Interpretability
Herman, B · 2017
Later among the works it cites.
Learning Interpretable Classification Rules with Boolean Compressed Sensing
Malioutov, D. M., Varshney, K. R., Emad, A., and Dash, S · 2017
Later among the works it cites.
Explainable AI: Beware of Inmates Running the Asylum
Miller, T., Howe, P., and Sonenberg, L · 2017
Later among the works it cites.
Ockham’s Razor Cuts to the Root: Simplicity in Causal Explanation
Pacer, M. and Lombrozo, T · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhou, Y. and Hooker, G · 2016
Cited alongside, same era.
Semantics Derived Automatically from Language Corpora Contain Human-Like Biases
Caliskan, A., Bryson, J. J., and Narayanan, A · 2017
Cited alongside, same era.
Interpretability of Deep Learning Models: A Survey of Results
Chakraborty, S., Tomsett, R., Raghavendra, R., Harborne, D., Alzantot, M., Cerutti, F., Srivastava, M., Preece, A., Julier, S., Rao, R. M., Kelley, Troy D., Braines, D., Sensoy, M., Willis, C. J., and Gurram, P · 2017
Cited alongside, same era.
Towards A Rigorous Science of Interpretable Machine Learning
Doshi-Velez, F and Kim, B · 2017
Cited alongside, same era.
UCI Machine Learning Repository, 2017
Dua, D. and Karra Taniskidou, E · 2017
Cited alongside, same era.
Rationalization: A Neural Machine Translation Approach to Generating Natural Language Explanations
Ehsan, U., Harrison, B., Chan, L., and Riedl, M. O · 2017
Cited alongside, same era.
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly
Elenberg, E. R., Dimakis, A. G., Feldman, M., and Karbasi, A · 2017
Cited alongside, same era.
Puri, N., Gupta, P., Agarwal, P., Verma, S., and Krishnamurthy, B · 2017
Later among the works it cites.
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
Later among the works it cites.
Axiomatic Attribution for Deep Networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Later among the works it cites.
Top-Down Neural Attention by Excitation Backprop
Zhang, J., Bargal, S. A., Lin, Z., Brandt, J., Shen, X., and Sclaroff, S · 2017
Later among the works it cites.
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
Dhurandhar, Amit, Chen, Pin-Yu, Luss, Ronny, Tu, Chun-Chen, Ting, Paishun, Shanmugam, Karthikeyan, and Das, Payel · 2018
Closest in time.
A Comparative Study of Fairness-Enhancing Interventions in Machine Learning
Friedler, S. A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E. P., and Roth, D · 2018
Closest in time.
A Survey Of Methods For Explaining Black Box Models
Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., and Giannotti, F · 2018
Closest in time.