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

arXiv: https://arxiv.org/abs/2507.15036 · HTML: https://mlsanity.com/paper/2507.15036/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 12 of 12)

- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [U-shape Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2111.11843/) (2021)
- [Enhancing Underwater Imagery using Generative Adversarial Networks](https://mlsanity.com/paper/1801.04011/) (2018)
- [PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators](https://mlsanity.com/paper/2306.08918/) (2023)
- [Energy Efficiency of Training Neural Network Architectures: An Empirical Study](https://mlsanity.com/paper/2302.00967/) (2023)
- [EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search](https://mlsanity.com/paper/2210.06015/) (2022)
- [Operating critical machine learning models in resource constrained regimes](https://mlsanity.com/paper/2303.10181/) (2023)
- [RAUNE-Net: A Residual and Attention-Driven Underwater Image Enhancement Method](https://mlsanity.com/paper/2311.00246/) (2023)
- [Towards Physical Plausibility in Neuroevolution Systems](https://mlsanity.com/paper/2401.17733/) (2024)
- [ADOD: Adaptive Domain-Aware Object Detection with Residual Attention for Underwater Environments](https://mlsanity.com/paper/2312.06801/) (2023)
- [Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes](https://mlsanity.com/paper/2410.00836/) (2024)

## Built on by (most cited first, 1 of 1)

- [FishDet-M: A Unified Large-Scale Benchmark for Robust Fish Detection and CLIP-Guided Model Selection in Diverse Aquatic Visual Domains](https://mlsanity.com/paper/2507.17859/) (2025)

## Abstract (arXiv)

Underwater image enhancement is vital for marine conservation, particularly coral reef monitoring. However, AI-based enhancement models often face dataset bias, high computational costs, and lack of transparency, leading to potential misinterpretations. This paper introduces EBA-AI, an ethics-guided bias-aware AI framework to address these challenges. EBA-AI leverages CLIP embeddings to detect and mitigate dataset bias, ensuring balanced representation across varied underwater environments. It also integrates adaptive processing to optimize energy efficiency, significantly reducing GPU usage while maintaining competitive enhancement quality. Experiments on LSUI400, Oceanex, and UIEB100 show that while PSNR drops by a controlled 1.0 dB, computational savings enable real-time feasibility for large-scale marine monitoring. Additionally, uncertainty estimation and explainability techniques enhance trust in AI-driven environmental decisions. Comparisons with CycleGAN, FunIEGAN, RAUNENet, WaterNet, UGAN, PUGAN, and UTUIE validate EBA-AI's effectiveness in balancing efficiency, fairness, and interpretability in underwater image processing. By addressing key limitations of AI-driven enhancement, this work contributes to sustainable, bias-aware, and computationally efficient marine conservation efforts. For interactive visualizations, animations, source code, and access to the preprint, visit: https://lyessaadsaoud.github.io/EBA-AI/