(untitled) (Lex localis - Journal of Local Self-Government, 2024)
The Sustainable Development Goals (SDGs) provide a detailed worldwide framework aimed at promoting sustainability while ensuring equity and building resilience. The Sustainable Development Solutions Network (SDSN) created the SDG Index which functions as a crucial standard for evaluating how nations advance toward their sustainable development goals. The SDG Index methodology contains unavoidable limitations such as overestimation risks due to inconsistent data and subjective normalization processes alongside unequal goal weighting. The research dissects methodological biases to reveal the contradiction between high national rankings and poor performance in essential sustainability areas even when aggregate scores appear strong. We introduce an advanced AI-based framework for SDG assessment that utilizes machine learning algorithms combined with predictive analytics to generate objective-specific indices with dynamic weights which provide a more detailed and data-driven evaluation of national advancement. The new framework uses AI-driven anomaly detection along with feature importance modelling and unsupervised clustering techniques to improve sustainability assessment fidelity while reducing distortions from traditional aggregation methods. Our research examines both epistemological and ethical questions that emerge from AI involvement in sustainability assessments while analyzing the consequences for worldwide policy creation and fair development discussions. The research findings highlight the need for a fundamental change in SDG measurement approaches by proposing AI-enhanced metrics as tools to improve transparency and methodological precision while increasing adaptability in global development evaluations.