\section{Related Work}

\paragraph{Reconstruction Models in Medical Imaging:}
Medical image reconstruction is a popular AI application due to its promise in increasing image quality while facilitating lower radiation doses and faster scanning times \cite{AHISHAKIYE2021118, Diab2025-fm}. Given pairs of noisy (i.e., undersampled/lower dose) and original images, these models are trained to reconstruct the original from the noisy image. Recently, unsupervised methods have also been developed that do not require paired original and noisy images \cite{Chen2023-vn, Sultan2025-co, CHEN2026102015}. Variations of the U-Net \cite{Unet} are commonly used as the neural network architecture for reconstruction models. In addition to standard losses like mean-squared error (MSE), GAN and diffusion-based approaches are common in the field \cite{bousse2024review, Heckel2024}.

\paragraph{Fairness Analysis in Medical Imaging:}
Research on bias in AI-driven healthcare spans various medical domains, with medical imaging receiving considerable attention. In classification tasks, biases are typically revealed by comparing performance across subgroups. Studies cover various imaging modalities, including brain MRI \cite{Stanley2022FairnessrelatedPA, ioannou2022studydemographicbiascnnbased}, chest X-rays \cite{Kalantari2021UnderdiagnosisCheX, Glocker2023AlgorithmicEO, Yang2024-dm, Lotter2024-sk}, dermatology images \cite{CHIU2024103188, groh2021evaluating}, and retinal images \cite{Burlina2021Retinal}. They address sensitive attributes such as sex \cite{Stanley2022FairnessrelatedPA}, age \cite{Kalantari2021UnderdiagnosisCheX}, race \cite{Kalantari2021UnderdiagnosisCheX}, and skin tone \cite{Kinyanyui2020Dermatology}, evaluating disparities using performance metrics such as Area Under the Curve (AUC) \cite{Kalantari2021UnderdiagnosisCheX}, or more dedicated fairness criteria \cite{jamanetworkopen.2023.42203}. In segmentation, studies have assessed segmentation performance under varying demographic distributions, such as by race and sex representation in training datasets \cite{ioannou2022studydemographicbiascnnbased,lee2022systematicstudyracesex,puyol2022fairness}.

\paragraph{Fairness Analysis of Reconstruction Models:}
Reconstruction model performance is typically measured using image quality metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). Recent studies assessing subgroup biases primarily rely on these metrics, examining how image quality varies across demographic subgroups. For instance, \citet{du2023unveilingfairnessbiasesdeep} investigated fairness in deep learning-based brain MRI reconstruction, highlighting disparities in image reconstruction quality across different demographic groups using PSNR and SSIM. Similarly, \citet{Sheg24reconbias} explored fairness challenges and potential solutions in ultrasound computed tomography, identifying significant disparities in reconstruction performance linked to subgroup attributes. With limited available literature, bias evaluation in reconstruction models is an emerging area of research for which there is a need to study the implications of image reconstruction on downstream tasks.

\paragraph{Bias Mitigation:}
In classification, substantial efforts have focused on developing bias mitigation strategies. Data-centric approaches directly modify training datasets, employing methods such as data redistribution \cite{Oguguo23}, differentiable resampling techniques \cite{repair}, harmonization of datasets \cite{bissoto2019deconstructingbiasskinlesion}, and synthetic generation of diverse samples \cite{WANG2024105047}. Additionally, methods like Just Train Twice (JTT) target misclassified instances to implicitly mitigate subgroup biases without explicit annotations \cite{JTT}.

Representation-level strategies aim to learn unbiased feature representations through explicit disentanglement. Techniques include variational autoencoders \cite{Creager2019FlexiblyFR}, orthogonal disentanglement methods enforcing independence between sensitive attributes and task-specific features \cite{Sarhan,WenlongOrtho,CHIU2024103188,FairDisCo}, and group-adaptive architectures employing demographic-specific attention mechanisms \cite{Gong}.

Optimization-level methods integrate fairness constraints into model training via adversarial learning, fairness-specific loss functions, or specialized training regimens. Adversarial methods discourage encoding protected attributes \cite{Zhang,Adeli2019RepresentationLW,KimKimKimKimKim,Wang}, distributionally robust optimization (Group DRO) targets worst-case subgroup performance \cite{DRO}, and fairness-specific constraints can be incorporated directly into training \cite{marcinkevičs2022debiasingdeepchestxray}. Post-processing methods adjust model outputs after training, employing techniques such as calibration and pruning \cite{wu2022fairpruneachievingfairnesspruning}.

While prior studies have focused mainly on bias mitigation in classification tasks, there remains a critical need to assess analogous strategies for image reconstruction.