ABOUT

There is a growing concern that machine learning contributes to the reproducibility crisis in science. We have reached a point where we can’t blindly trusts discoveries made using machine learning. An executive may be hesitant to make a decision based on automated recommendation that cannot be reproduced, but even more concerning is the state of reproducibility in fields where machine learning algorithms are used to monitor and intervene in the context of human health. In this session we try to define and look at the impact of inconsistencies in predictions delivered by machine learning algorithms, and we cover how questions and insights we gather from actively listening to industry help our platform to deliver consistent and reproducible results.