Cached Neighborhood Evidence Gives kNN Classifiers a Fairer, Faster Way to Handle Rare Classes
A new classifier called CABLE-kNN caches local neighborhood evidence during training and neutralizes class priors, beating ordinary kNN on imbalanced ...
A new classifier called CABLE-kNN caches local neighborhood evidence during training and neutralizes class priors, beating ordinary kNN on imbalanced ...
A new philosophical framework distinguishes remediable algorithmic bias from discrimination built into a system's representational choices, and proposes upstream ontological ...
A new study combines graph neural networks with explainable AI to map how Laotian migrant workers in Bangkok build and ...
A new study argues that self-driving cars may sometimes need to break speed limits to stay safe, but only within ...
A new fairness-regularized framework called CoR-Hate retrieves real counterfactual examples from corpus data to reduce identity bias in hate-speech detection ...
Researchers have built an explainable, fairness-audited early warning system that predicts online student dropout using only the first weeks of ...
Researchers have developed SLF–FST, a stress-testing framework that injects progressive label bias into training data and reveals that Random Forests ...
A systematic analysis of 938 trustworthy AI tools and certification schemes in the OECD catalogue reveals that transparency, fairness, and ...
Researchers in Gaza show that internet-induced exam failures leave detectable signatures in learning management system logs, allowing institutions to separate ...
A large-scale computational review of 3,268 works maps the rapidly shifting landscape of AI research in the public sector and ...
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© 2025 Scienmag - Science Magazine