Page 5: 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.

Business

Detecting fraudulent product reviews with enhanced accuracy

The rise of e-commerce has brought unprecedented convenience to consumers, but it has also created fertile ground for deceptive practices in online marketplaces. A growing body of research is now focusing on the detection ...

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 ...

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