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215 papers
Emerging research directions, such as explainable AI, federated learning, digital twins, edge intelligence, and physics-informed machine learning, are identified as promising strategies for developing resilient, intelligent, and sustainable future power grids.
The results indicate that sustainable performance is achieved not merely through AI adoption but through the organization's ability to sense opportunities, seize strategic initiatives and continuously reconfigure resources, thereby extending RBV and DCT within the sustainability and digital transformation domains.
In light of the pervasive methodological limitations identified, including high analytic risk of bias, absence of external validation, and lack of model interpretability, claims of ML superiority over CHA2DS2-VASc must be interpreted with caution.
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This narrative review article uniquely integrates current and emerging applications of AI across full SCS pathway while also further critically highlighting evidence gaps, future directions for precision neuromodulation.
Algorithmic fairness and explainability are foundational pillars of responsible AI. Although often studied independently, their interplay is increasingly recognized as crucial for diagnosing and mitigating bias in machine learning systems. We first introduce two systematic taxonomies: one for algorithmic fairness and one for explainable AI, to organize the landscape of existing work across diverse tasks (classification, ranking, and recommendation) and data modalities (tabular, graph). Next, we categorize the use of explanations in fairness efforts into three main functions: (a) detecting and understanding the causes of unfairness, (b) defining enhanced fairness metrics, and (c) designing mitigation strategies. In addition, we examine how explanation methods themselves can be biased, underscoring the need to evaluate fairness for explanations. Finally, we identify open research challenges and outline promising directions for future research at the intersection of fairness and explainability.
Earth AI
Piloting the world's first double-blind AI evaluations
The “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.
Generative AI
Google DeepMind partners with game studios to prototype breakthrough AI gameplay.
