AI security blog

Nobel Prize-winning economist, Daniel Kahneman once remarked “by their very nature, heuristic shortcuts will produce biases, and that is true for both humans and artificial intelligence, but their heuristics of AI are not necessarily the human ones”. This is certainly the case when we talk about “shortcut learning”.

Unforeseen data may be presented to the computer vision system during operation despite careful mitigation of datasets and shortcuts.

The promise these possibilities hold has put medical imaging in the lead of the race toward landing in hospitals. But that is not the end of the discussion…

Sounds like a lot of work? It used to be, but with the advent of artificial intelligence (AI) observability software, such assessments become as easy as training a new model.

Will my system work if image quality starts to drop significantly? If my system works at a given occlusion level, how much stronger can occlusion get before the system starts to underperform? I have faced such issues repeatedly in the past, all related to an overarching question: How robust is my model and when does it break?

Deep-dive into advanced comparison methods beyond standard performance metrics to build computer vision models that consistently perform over the long term.