Transparency and reproducibility in artificial intelligence

By October 14, 2020October 26th, 2020Publications

Breakthroughs in artificial intelligence (AI) hold enormous potential as it can automate complex tasks and go even beyond human performance. In their study, McKinney et al.1 showed the high potential of AI for breast cancer screening. However, the lack of details of the methods and algorithm code undermines its scientific value. Here, we identify obstacles that hinder transparent and reproducible AI research as faced by McKinney et al.1, and provide solutions to these obstacles with implications for the broader field.