Indeed, Adversarial Artificial Intelligence (AI) which refers to a set of techniques that attempt to fool machine learning models with deceptive data, is a growing threat in the AI and machine learning research community, in particular for machine-critical applications.
Conversational Recommendation Systems (CRSs) have recently started to leverage pretrained language models (LM) such as BERT for their ability to semantically interpret a wide range of preference statement variations.
These use an external corpus as a knowledge base that conditions the model to help predict what a document is about.
Combining symbolic human knowledge with neural networks provides a rule-based ante-hoc explanation of the output.
Collaborative Filtering (CF) is one of the most commonly used recommendation methods.