%0 Journal Article %1 Rudin_2019 %A Rudin, Cynthia %D 2019 %I Springer Science and Business Media LLC %J Nature Machine Intelligence %K artificial-intelligence explainable-ai machine-learning %N 5 %P 206--215 %R 10.1038/s42256-019-0048-x %T Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead %U https://doi.org/10.1038%2Fs42256-019-0048-x %V 1

@article{Rudin_2019, added-at = {2019-12-18T19:08:13.000+0100}, author = {Rudin, Cynthia}, biburl = {https://www.bibsonomy.org/bibtex/2f72434f1e6a9f73072c73cfa933ecd75/nonancourt}, doi = {10.1038/s42256-019-0048-x}, interhash = {5d4eb0661227735aec23f7981e8b0849}, intrahash = {f72434f1e6a9f73072c73cfa933ecd75}, journal = {Nature Machine Intelligence}, keywords = {artificial-intelligence explainable-ai machine-learning}, month = may, number = 5, pages = {206--215}, publisher = {Springer Science and Business Media {LLC}}, timestamp = {2019-12-18T19:08:13.000+0100}, title = {Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead}, url = {https://doi.org/10.1038%2Fs42256-019-0048-x}, volume = 1, year = 2019 }