We study fairness in collaborative-filtering recommender systems, which are sensitive to discrimination that exists in historical data. Biased data can lead collaborative-filtering methods to make unfair predictions for users from minority groups. We identify the insufficiency of existing fairness metrics and propose four new metrics that address different forms of unfairness. These fairness metrics can be optimized by adding fairness terms to the learning objective. Experiments on synthetic and real data show that our new metrics can better measure fairness than the baseline, and that the fairness objectives effectively help reduce unfairness.

Cognitive processes usually take place at a macroscopic scale in systems charac-terised by emergent properties that make the whole'more than the sum of its parts.'

This work investigates how these complexities necessarily arise for feature learning in the presence of computational-statistical gaps.

The development of efficient machine learning models for molecular systems representation is becoming crucial in scientific research.