1. Adverse Events in Robotic Surgery: A Retrospective Study of 14 Years of FDA Data
2. Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending
3. Arya, V., Bellamy, R. K. E., Chen, P.Y., Dhurandhar, A., Hind, M., Hoffman, S. C., Houde, S., Liao, Q. V., Luss, R., Mojsilović, A., Mourad, S., Pedemonte, P., Raghavendra, R., Richards, J., Sattigeri, P., Shanmugam, K., Singh, M., Varshney, K. R., Wei, D. & Zhang, Y. (2019). One explanation does not fit all: A toolkit and taxonomy of ai explainability techniques. arXiv, 1909.03012.
4. Azevedo, A. & Santos, M. F. (2008). KDD, SEMMA and CRISP-DM: a parallel overview. In IADIS European Conf. Data Mining. https://www.semanticscholar.org/paper/KDD%2C-SEMMA-and-CRISP-DM%3A-a-parallel-overview-Azevedo-Santos/6bc30ac3f23d43ffc2254b0be24ec4217cf8c845. Europ. Conf. Data Mining (IADIS) (pp. 182–185).