The Problematic of Objectivity and Bias in Digital Systems A Critical Analysis and Practical Applications

concerns about digital bias. AI systems, while efficient, can inherit and amplify biases present in training data and algorithmic design, leading to unfair outcomes. This study explores the extent to which AI can be considered objective by identifying key sources of bias and assessing their societal impact.

Using a multidisciplinary approach, the research examines algorithmic bias in employment, justice, finance, and healthcare. The findings highlight the risks of biased AI and emphasize the need for regulatory frameworks, bias-mitigation techniques, and explainable AI (XAI) to ensure fairness, transparency, and accountability in digital decision-making.

Keywords: Digital Bias; Algorithmic Fairness; Explainable AI (XAI); AI Accountability; Transparency in Digital Systems