Is it better to have a high performing black box AI or a lower performing explainable AI?
Are the explanations for how AI works actually true or really a distortion of what's going on inside the model?
How should we think about and operationalize the tradeoffs between running explainability algos and the high cost and carbon footprint of running them?
These questions and more with Kristof. A really fascinating discussion that reveals the complexity behind simplistic calls for explainable AI.
Kristof is currently leading Responsible AI at JPMorgan Chase. Previously, he helped build out and subsequently led the Responsible AI effort at PayPal. He held various other quantitative roles at major banks and has a bachelors in mathematics and a masters in applied mathematics from the Budapest University of Technology and Economics.