120 research outputs found
Mitigating subjectivity and bias in AI development indices: A robust approach to redefining country rankings
Countries worldwide have been implementing different actions national
strategies for Artificial Intelligence (AI) to shape policy priorities and
guide their development concerning AI. Several AI indices have emerged to
assess countries' progress in AI development, aiding decision-making on
investments and policy choices. Typically, these indices combine multiple
indicators using linear additive methods such as weighted sums, although they
are limited in their ability to account for interactions among indicators.
Another limitation concerns the use of deterministic weights, which can be
perceived as subjective and vulnerable to debate and scrutiny, especially by
nations that feel disadvantaged. Aiming at mitigating these problems, we
conduct a methodological analysis to derive AI indices based on multiple
criteria decision analysis. Initially, we assess correlations between different
AI dimensions and employ the Choquet integral to model them. Thus, we apply the
Stochastic Multicriteria Acceptability Analysis (SMAA) to conduct a sensitivity
analysis using both weighted sum and Choquet integral in order to evaluate the
stability of the indices with regard the weights. Finally, we introduce a novel
ranking methodology based on SMAA, which considers several sets of weights to
derive the ranking of countries. As a result, instead of using predefined
weights, in the proposed approach, the ranking is achieved based on the
probabilities of countries in occupying a specific position. In the
computational analysis, we utilize the data employed in The Global AI Index
proposed by Tortoise. Results reveal correlations in the data, and our approach
effectively mitigates bias. In the sensitivity analysis, we scrutinize changes
in the ranking resulting from weight adjustments. We demonstrate that our
proposal rankings closely align with those derived from weight variations,
proving to be more robust
Towards sustainability: An assessment of an urbanisation bubble in China using a hierarchical - stochastic multicriteria acceptability analysis - Choquet integral method
Urbanisation bubbles have become an increasingly serious problem. Attention has been paid to the speed of urbanisation; however, the issue of quality has been neglected, particularly in the case of China. Therefore, the aim of this research is to evaluate China’s urbanisation bubbles by employing a hierarchical - stochastic multicriteria acceptability analysis (SMAA) - Choquet integral method. In order to highlight regional disparities, we measure the urbanisation bubbles at a provincial level. Our study aggregates the urbanisation bubble indices using the Choquet integral preference model, and considers the interactions between various indicators. Furthermore, robust ordinal regression and SMAA are applied to resolve the robustness issues associated with the entire set of weights assigned to the urbanisation bubble composite indicator. In addition, by employing a multiple criteria hierarchy process, the study aggregates urbanisation bubble indices not only at the comprehensive level, but also at the intermediate levels of the hierarchy. Our findings suggest that the ranking of urbanisation bubbles is positively related to the level of regional development. This study contributes to the evaluation of regional urbanisation and sustainable development
Towards sustainability: An assessment of an urbanisation bubble in China using a hierarchical - Stochastic multicriteria acceptability analysis - Choquet integral method
Urbanisation bubbles have become an increasingly serious problem. Attention has been paid to the speed of urbanisation; however, the issue of quality has been neglected, particularly in the case of China. Therefore, the aim of this research is to evaluate China's urbanisation bubbles by employing a hierarchical - stochastic multicriteria acceptability analysis (SMAA) - Choquet integral method. In order to highlight regional disparities, we measure the urbanisation bubbles at a provincial level. Our study aggregates the urbanisation bubble indices using the Choquet integral preference model, and considers the interactions between various indicators. Furthermore, robust ordinal regression and SMAA are applied to resolve the robustness issues associated with the entire set of weights assigned to the urbanisation bubble composite indicator. In addition, by employing a multiple criteria hierarchy process, the study aggregates urbanisation bubble indices not only at the comprehensive level, but also at the intermediate levels of the hierarchy. Our findings suggest that the ranking of urbanisation bubbles is positively related to the level of regional development. This study contributes to the evaluation of regional urbanisation and sustainable development
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