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Algorithmic decision-making systems have become pervasive across critical domains including employment, housing, healthcare, and criminal justice. While these systems promise enhanced efficiency and objectivity, they increasingly demonstrate patterns of discrimination that perpetuate and amplify existing societal biases. This paper examines...

Artificial intelligence systems deployed through cloud infrastructure have transformed numerous sectors while simultaneously raising critical ethical concerns regarding bias and fairness. This article examines the multifaceted nature of algorithmic bias in cloud AI systems, presenting quantitative evidence of disparities across...

Machine learning fairness enhancement methods based on data bias correction are usually divided into two processes: The determination of sensitive attributes (such as race and gender) and the correction of data bias. In terms of determining sensitive attributes, existing studies...

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Databases and Information Systems Integration, Artificial Intelligence and Decision Support Systems, Information Systems Analysis and Specification, Software Agents and Internet Computing, Human-Computer Interaction, Enterprise Architecture

arXiv:2602.12389v1 Announce Type: new Abstract: Temporal knowledge graph (TKG) forecasting requires predicting future facts by jointly modeling structural dependencies within each snapshot and temporal evolution across snapshots. However, most existing methods are stateless: they recompute entity representations at each timestamp...

arXiv:2602.13093v2 Announce Type: new Abstract: Large reasoning models with reasoning capabilities achieve state-of-the-art performance on complex tasks, but their robustness under multi-turn adversarial pressure remains underexplored. We evaluate nine frontier reasoning models under adversarial attacks. Our findings reveal that reasoning...

arXiv:2602.12305v1 Announce Type: cross Abstract: Generating high-performance CUDA kernels remains challenging due to the need to navigate a combinatorial space of low-level transformations under noisy and expensive hardware feedback. Although large language models can synthesize functionally correct CUDA code, achieving...

arXiv:2602.12384v2 Announce Type: cross Abstract: Understanding why gradient-based training in deep networks exhibits strong implicit bias remains challenging, in part because tractable singular-value dynamics are typically available only for balanced deep linear models. We propose an alternative route based on...

arXiv:2602.12393v1 Announce Type: cross Abstract: DragDiffusion is a diffusion-based method for interactive point-based image editing that enables users to manipulate images by directly dragging selected points. The method claims that accurate spatial control can be achieved by optimizing a single...