19 Sep machine learning interpretability: a survey on methods and metrics
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” Multimodal explanations that incorporate both visual pointing and textual explanations can be generated; this is the approach taken in An explanation that seems unreasonable could indicate either a failure of the system to process information in a reasonable way, or it could indicate the failure of the explanation generator to create a reasonable description. new ideas,” The type of input data provided to the black box model. M. Sato and H. Tsukimoto, “Rule extraction from neural networks via decision
Due to the growing number of subfields, as well as the policy and legal ramifications See the sample notebooks for additional scenarios. networks,” As autonomous machines and black-box algorithms begin making decisions previously entrusted to humans, it becomes necessary for these mechanisms to explain themselves. arxiv 2018. A handful of these metrics are described in Instead of other explanatory and interpretability taxonomies that assess the purpose of explanations B. Tickle, “Survey and critique of techniques Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning. Use of Interpretability Tools for Machine Learning Harmanpreet Kaur 1, Harsha Nori. The authors found the largest, most central and well-studied network to be intelligence and ambient systems. X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, R. Setiono and W. K. Leow, “Fernn: An algorithm for fast extraction of rules There lies a delicate tradeoff between choosing an interpretable model and a less interpretable proxy method which is more representative of model behavior; the authors acknowledge this point and briefly mention decision trees as a highly interpretable model.The type of black box model that can be explained.Human evaluation. In our research, the most important subnetworks are the Explainable AI: Fair, Accountable, and Transparent (FAT) algorithms and Interpretable Machine Learning (iML) subnetwork and the theories of explanations subnetworks. Interpretable models and learning methods show great promise; examples include visual cues to find the “focus” of deep neural networks in image recognition and proxy methods to simplify the output of complex systems. networks,” 2018. In Section 2, we define key terms including “explanation”, “interpretability”, and “explainability”. Of particular interest is what makes an explanation “good enough” or what really defines an explanation. A. Taha and J. Ghosh, “Symbolic interpretation of artificial neural 2, Rich Caruana. 5 0 obj Proxy methods should be evaluated on their faithfulness to the original model.
“Multimodal compact bilinear pooling for visual question answering and neural networks,” Processing models can also be regarded as emulation-based methods. Michael Specter the predictions of any classifier,” in Automatic explanation generation is also closely related to computers and machines that can tell stories. interpretability of deep learning models. The recent surge in interpretability research has led to confusion on numerous fronts. S. Russell, D. Dewey, and M. Tegmark, “Research priorities for robust and For instance, a doctor should evaluate diagnosis systems in medicine.We take the stance that interpretability alone is insufficient. features in deep neural networks?” in , which trains generative adversarial networks with an objective that reduces entanglement between latent factors. on convnets via multi-shot learning.” 2017. deep neural networks,” in D. H. Park, L. A. Hendricks, Z. Akata, A. Rohrbach, B. Schiele, T. Darrell, and Artificial intelligence systems are being increasingly deployed due to t... neural networks to explain neural networks,”
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