EBA-AI: Ethics-Guided Bias-Aware AI for Efficient Underwater Image Enhancement and Coral Reef Monitoring

Bias in AI threatens conservation decisions: The paper demonstrates that standard AI models for underwater image analysis suffer from dataset bias that can lead to misinterpretations of coral reef data. EBA-AI addresses this by using CLIP embeddings to detect and balance representation across different underwater environments, ensuring that AI-driven conservation decisions aren't skewed by incomplete training data.

Efficiency gains enable real-world marine monitoring: By reducing GPU usage through adaptive processing while accepting only a controlled 1.0 dB drop in image quality (PSNR), EBA-AI makes large-scale coral reef monitoring computationally feasible. This efficiency breakthrough means conservation organizations with limited resources can now deploy AI monitoring at scale rather than being restricted to small pilot projects.

Transparency and explainability build trust in environmental AI: The framework incorporates uncertainty estimation and explainability techniques alongside its technical improvements. This addresses a critical gap in responsible AI for conservation—decision-makers and stakeholders need to understand why an AI system flagged a reef as degraded, not just that it did.

Balancing multiple responsible AI principles is possible: Rather than treating efficiency, fairness, and interpretability as competing goals, EBA-AI demonstrates they can be integrated into a single framework. This shows that "responsible AI" doesn't require choosing between environmental impact (computational costs) and ethical concerns (bias and transparency).