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Could we over-rely on early evaluation results or AI-generated outputs?
Could we over-rely on early evaluation results or AI-generated outputs?
Biases can emerge during the evaluation and validation stages of AI models, especially when over-relying on early test results or automated AI decisions. This can lead to misleading conclusions. Specific biases include:
- Evaluation bias: when chosen metrics don't align with the model’s real-world application.
- Anchoring bias: when too much focus is placed on initial results.
- Automation bias: when excessive trust is placed in AI outputs. Even in less risky phases like validation or monitoring, biases can develop. For instance, during the monitoring phase, reinforcing feedback loops can occur when biased model outputs are fed back into the system, amplifying distortions over time.
If you answered Yes then you are at risk
If you are not sure, then you might be at risk too
Recommendations
- Tailor evaluation metrics to the model and target population, and watch for overfitting across different groups.
- Identify performance gaps between groups and adjust for data imbalances to ensure fairness.
- Limit reliance on initial results; test across diverse datasets for robustness.
- Include human oversight in validation to prevent over-trust in AI decisions.
- Monitor model performance post-deployment to catch biases or feedback loops early.
- Address data drift regularly to maintain model fairness and accuracy.