Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts

Mansfield, Laura A.; Gupta, Aman; Burnett, Adam C.; Green, Brian; Wilka, Catherine; Sheshadri, Aditi

doi:10.1029/2023ms003715

Cited by 5 publications

(1 citation statement)

References 137 publications

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“…Quantitative data was further supported by qualitative interviews, which aimed to measure the extent to which the individual sentences were satisfactory with the type of explanation produced by each algorithm [27]. The respondents were asked to give easy, useful, and dependable explanations scores on a Likert scale.…”

Section: Human Satisfaction Evaluationmentioning

confidence: 99%

Explainable AI: Bridging the Gap between Machine Learning Models and Human Understanding

2024

Journal of Informatics Education and Research

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Explainable AI (XAI) is one of the key game-changing features in machine learning models, which contribute to making them more transparent, regulated and usable in different applications. In (the) investigation of this paper, we consider the four rows of explanation methods-LIME, SHAP, Anchor, and Decision Tree-based Explanation-in disentangling the decision-making process of the black box models within different fields. In our experiments, we use datasets that cover different domains, for example, health, finance and image classification, and compare the accuracy, fidelity, coverage, precision and human satisfaction of each explanation method. Our work shows the rule trees approach called (Decision Tree-based explanation) is mostly superior in comparison to other non-model-specific methods of explanation performing higher accuracy, fidelity, coverage and precision regardless the classifier. In addition to this, the respondents who answered the qualitative evaluation indicated that they were very content with the decision tree-based explanations and that these types of explanations are very easy and understandable. Furthermore, most of the respondents famous that these sorts of clarifications are more instinctive and significant. The over discoveries stretch on the utilize of interpretable AI strategies for facilitating the hole between machine learning models and human understanding and thus advancing straightforwardness and responsibility in AI-driven decision-making.

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Section: Human Satisfaction Evaluationmentioning

confidence: 99%

Explainable AI: Bridging the Gap between Machine Learning Models and Human Understanding

2024

Journal of Informatics Education and Research

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Comparison of indicators to evaluate the performance of climate models

Gómez,

Barboza,

Hidalgo

et al. 2024

Intl Journal of Climatology

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The evaluation of climate models is a crucial step in climate studies. It consists of quantifying the resemblance of model outputs to reference data to identify models with superior capacity to replicate specific climate variables. Clearly, the choice of the evaluation indicator significantly impacts the results, underscoring the importance of selecting an indicator that properly captures the characteristics of a “good model”. This study examines the behaviour of six indicators, considering spatial correlation, distribution mean, variance and shape. Monthly data for precipitation, temperature and teleconnection patterns in Central America were utilized in the analysis. A new multicomponent measure was selected based on these criteria to assess the performance of 32 CMIP6 models in reproducing the annual seasonal cycle of these variables. The top six models were determined using multicriteria methods. It was found that even the best model reproduces one derived climatic variable poorly in this region. The proposed measure and selection method can contribute to enhancing the accuracy of climatological research based on climate models.

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