Which accountability mechanism BEST addresses the 'black box' problem for high-stakes AI decisions affecting individuals?
Select an answer to reveal the explanation.
Short Explanation
Here's the deal — the black box problem is best fixed when you give people an explanation for their own decision, not just overall stats. That lets them understand why it happened and challenge it.
Full Explanation
The correct mechanism is providing individualized explanations of the factors that influenced each specific decision. The black box problem — where individuals cannot understand why a decision was made — is best addressed by individualized, decision-specific explanations (such as SHAP values translated to natural language), enabling subjects to understand and contest specific decisions affecting them. Publishing aggregate model performance statistics on a public dashboard provides aggregate transparency, not individual accountability. Open-sourcing the model's training code on GitHub enables technical review but not layperson understanding of individual decisions. Requiring data scientists to document their feature engineering choices improves developer documentation, not decision-level accountability.