Bias, Fairness and the Machine in the Sustainable Digital Ecosystem: An Analytical Survey on Confronting Inequality in the Digital World
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Abstract
The bursting growth of digital technologies has changed the way we classify, assess risk and allocate resources. Algorithmic models are increasingly operating as infrastructures of measurement in contemporary institutions. This research builds a theoretical accounting, derived accounting perspective to explore algorithmic bias and fairness in the sustainable digital ecosystems. Through analytical synthesis of interdisciplinary scholarship and documented empirical cases, the paper conceives algorithmic systems as extensions of accounting infrastructures with functions of recognition, measurement and classification. It argues that the sorts of biases built into the processes of data selection, proxy-based measurement and optimization represent forms of representational distortion like misstatement in accounting systems. Integrating together the distributive justice, capability theory and accountability scholarship, the study shows that the issue of algorithmic fairness is not a technical problem, but essentially a governance and institutional design problem. Comparative analysis on regulatory developments shows emerging dependency on risk-based classification, disclosure requirements and audit like oversight, and ongoing lack of developed areas of enforceability and contestability. The paper concludes that sustainable algorithmic governance would need structured accountability mechanisms that embed auditability, participatory oversight and contextual regulatory integration which would extend measurement, audit, and sustainability accounting theory into the field of algorithmic decision-making.
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