Page 10: 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

AI approach developed with human decision-makers in mind

As artificial intelligence takes off, how do we efficiently integrate it into our lives and our work? Bridging the gap between promise and practice, Jann Spiess, an associate professor of operations, information, and technology ...

Computer Sciences

Team teaches AI models to spot misleading scientific reporting

Artificial intelligence isn't always a reliable source of information: large language models (LLMs) like Llama and ChatGPT can be prone to "hallucinating" and inventing bogus facts. But what if AI could be used to detect ...

Computer Sciences

Microsoft AI weather forecast faster, cheaper, truer: Study

Microsoft has developed an artificial intelligence (AI) model that beats current forecasting methods in tracking air quality, weather patterns, and climate-addled tropical storms, according to findings published Wednesday.

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