Assessment of Visual Computing Models Across Various Environments Concerning Bias and Disparity

Visual computing systems are being used more in high stakes applications such as self-driving, medical diagnostics, and identity verification. Nevertheless, there are still systematic issues in the evaluation of these systems especially in terms of bias, subgroup disparity, robustness and environmental variability. This paper is a systematic review of 28 articles (2010-2024) about evaluation in computer vision, identified through a PRISMA-guided screening process across major scientific databases. The review demonstrates over-dependence on benchmark-based measures, inadequate subgroup- disaggregated reporting, inconsistent robustness protocols, as well as inadequate environmental validation. These loopholes lead to differences in performance among the demographic groups and extensive degradation in the distributions under field scenarios. Through the synthesis of common methodological drawbacks in different tasks involved in detection, recognition, segmentation, and tracking, the review shows that the aspects of fairness, robustness, and generalization are not separate issues but are mutually related. In order to overcome this fragmentation, the paper recommends a lifecycle-based evaluation framework that would combine dataset auditing, subgroup-based, performance reporting, environmental testing, and constant monitoring in a single assessment process. The results emphasize the necessity to shift towards multidimensional criteria of evaluation that would be able to relate technical performance to the realities of the society and operations. This literature offers researchers and practitioners a systematic framework on how they could build more equal, trustworthy and context sensitive visual computing systems.