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Interpretable Machine Learning Tools: A Survey

2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
In recent years machine learning (ML) systems have been deployed extensively in various domains. But most MLbased frameworks lack transparency. To believe in ML models, an individual needs to understand the reasons behind the ML predictions. In this paper, we provide a survey of open-source software tools that help explore and understand the behavior ...
Namita Agarwal, Saikat Das
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Algorithms for interpretable machine learning

Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014
It is extremely important in many application domains to have transparency in predictive modeling. Domain experts do not tend to prefer "black box" predictive model models. They would like to understand how predictions are made, and possibly, prefer models that emulate the way a human expert might make a decision, with a few important variables, and a ...
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Interpretable machine learning

2020
Machine learning (ML, a type of artificial intelligence) is increasingly being used to support decision making in a variety of applications including recruitment and clinical diagnoses. While ML has many advantages, there are concerns that in some cases it may not be possible to explain completely how its outputs have been produced. This POSTnote gives
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A study on interpretability of decision of machine learning

2017 IEEE International Conference on Big Data (Big Data), 2017
Machine learning is one of the most important fields in recent improvement in big data analysis. Many people apply machine learning for a variety of domains for various purposes, such as classification of opinions. However, the constructed models of machine learning are black boxes. They cannot understand the background reason for their decisions.
Shohei Shirataki, Saneyasu Yamaguchi
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Interpretable machine learning assessment