Page 8: Research news on Trustworthy machine learning

Trustworthy machine learning addresses methods for training and deploying models that are secure, privacy-preserving, and robust to manipulation. Work in this area develops federated and decentralized learning schemes, cryptographic and homomorphic encryption frameworks, and privacy-preserving compression to protect data and models. It also studies adversarial example generation and defenses, certified unlearning, bias and spurious correlation mitigation, and the use of synthetic and filtered data. Applications span fraud and cyberattack detection, fake news and deception detection, and secure automation systems.

Computer Sciences

Platform can make machine learning more transparent and accessible

What began as a Ph.D. project has grown into a website with 120,000 unique visitors each year. With the platform OpenML, researcher Jan van Rijn is contributing to open science, aiming to make machine learning more transparent, ...

Machine learning & AI

Democratizing AI-powered sentiment analysis

Artificial intelligence is accelerating at breakneck speed, with larger models dominating the scene—more parameters, more data, more power. But here is the real question: Do we really need bigger to be better? We challenged ...

Hi Tech & Innovation

Researcher develops generative learning model to predict falls

In a study published in the journal Information Systems Research, Texas Tech University's Shuo Yu and his collaborators developed a generative machine learning model to detect instability before a fall occurs. The hope is ...

Energy & Green Tech

Practical changes could reduce AI energy demand by up to 90%

Artificial intelligence (AI) can be made more sustainable by making practical changes, such as reducing the number of decimal places used in AI models, shortening responses, and using smaller AI models, according to research ...

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