We are currently exploring machine learning approaches for algorithmically scoring key products associated with the Hogan assessments. Machine learning techniques provide improved predictive validity when applied appropriately; however, we at Hogan understand the importance of interpretability and transparency of our algorithms. Knowing why someone scored lower or higher is just as important as doing so accurately. Our algorithms are both interpretable and accurate.
We are currently exploring and using narrow AI to streamline our behind-the-scene processes. One example of narrow AI use at Hogan is applying natural language processing to improve our accuracy and speed with coding focus-group notes.