hard · Frm Part 2 Current Issues
A bank uses a black-box neural network for credit limit decisions. To provide 'adverse action notices,' it employs a local surrogate model technique. A validator finds that for a single applicant, two different 'post-hoc' explainers provide conflicting reasons for rejection.
This phenomenon highlights which specific risk in XAI validation?
- A violation of 'demographic parity,' where the model is rejecting candidates based on group-level features rather than individual risk profiles.
- The lack of 'local fidelity,' where the surrogate model fails to accurately map the complex model's behavior in the specific region of that applicant's data.
- The 'exclusion fallacy,' where the model has reconstructed a protected attribute through proxies that the explainers are unable to detect.
- Overfitting in the training phase, which prevents the explainers from converging on a stable feature importance ranking.
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