Fair decision-making in Machine Learning (ML) remains a critical challenge, particularly when access to sensitive information is restricted due to legal, ethical, or organizational constraints. These limitations affect both accuracy and fairness, creating tradeoffs central to the deployment of ML systems in the real world. While prior work has studied fairness-accuracy tradeoffs, most approaches focus on model outputs rather than directly examining how restricted data access impacts fairness. This leaves an important gap: understanding how fairness constraints affect model performance under real-world data restrictions . To address this gap, we propose a framework that explicitly models fairness-accuracy tradeoffs in data-restricted environments. Unlike prior work, our approach analyzes the behavior of the optimal Bayesian classifier using a discrete approximation of the data distribution, allowing us to systematically isolate the effects of fairness constraints. We evaluate our framework on three benchmark datasets—Adult, Law, and Dutch Census—revealing key insights: (1) enforcing equal accuracy on imbalanced datasets can substantially degrade performance under additional fairness constraints, (2) individual and group fairness often impose conflicting constraints, and (3) decorrelating sensitive attributes from features does not usually reduce accuracy. These findings demonstrate that our framework provides an effective, structured approach for practitioners to assess fairness constraints in decision-making pipelines.

Increases in the deployment of machine learning algorithms for applications that deal with sensitive data have brought attention to the issue of fairness in machine learning. Many works have been devoted to applications that require different demographic groups to be treated fairly. However, algorithms that aim to satisfy inter-group fairness (also called group fairness) may inadvertently treat individuals within the same demographic group unfairly. To address this issue, this article introduces a formal definition of within-group fairness that maintains fairness among individuals from within the same group. A pre-processing framework is proposed to meet both inter- and within-group fairness criteria with little compromise in performance. The framework maps the feature vectors of members from different groups to an inter-group fair canonical domain before feeding them into a scoring function. The mapping is constructed to preserve the relative relationship between the scores obtained from the unprocessed feature vectors of individuals from the same demographic group, guaranteeing within-group fairness. This framework has been applied to the Adult, COMPAS risk assessment, and Law School datasets, and its performance is demonstrated and compared with two regularization-based methods in achieving inter-group and within-group fairness.

The multi-plane image (MPI) is a promising framework for enabling novel view synthesis for 3D scene reconstruction. However, inefficient utilization of the MPI layers traditionally causes low compression efficiency, a large memory footprint, and high computational complexity. To address this issue, this paper proposes a framework for decreasing the number of layers in an MPI while producing high quality rendered images. We introduce a principled layer merging algorithm to reduce the number of layers in an MPI by using a constrained Lloyd-Max optimization formulation for determining how the layers should be merged. Since an imperfect MPI generation model may produce undesired artifacts in rendered images, which may be exacerbated after layer merging has been applied, a novel enhancement algorithm is constructed and applied before and after layer merging to remove artifacts and ensure the high quality of the layer-merged MPIs. Experimental results verify the utility of our framework, showing that: (1) our enhancement algorithm can significantly improve the quality of the MPI generated data, with an average increase in peak signal-to-noise ratio (PSNR) of $28 \sim 29$ dB and (2) our layer merging algorithm can successfully reduce the number layers in an MPI while producing high quality rendered images.