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.

Machine learning & AI

3 questions: The pros and cons of synthetic data in AI

Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources. While concrete numbers are hard to pin down, some estimates ...

Security

Fairness tool catches AI bias early

Machine learning software helps agencies make important decisions, such as who gets a bank loan or what areas police should patrol. But if these systems have biases, even small ones, they can cause real harm. A specific group ...

Software

A new way to test how well AI systems classify text

Is this movie review a rave or a pan? Is this news story about business or technology? Is this online chatbot conversation veering off into giving financial advice? Is this online medical information site giving out misinformation?

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