Recommending Dream Jobs in a Biased Real World
This work addresses bias in job recommendations for LinkedIn's global user base, but it appears incremental as it focuses on applying known techniques rather than introducing a fundamentally new approach.
The paper tackles the problem of bias in recommender systems, which can propagate societal biases like the professional gender gap into job recommendations, and proposes specific techniques to reduce bias at various stages of the system to improve fairness and effectiveness.
Machine learning models learn what we teach them to learn. Machine learning is at the heart of recommender systems. If a machine learning model is trained on biased data, the resulting recommender system may reflect the biases in its recommendations. Biases arise at different stages in a recommender system, from existing societal biases in the data such as the professional gender gap, to biases introduced by the data collection or modeling processes. These biases impact the performance of various components of recommender systems, from offline training, to evaluation and online serving of recommendations in production systems. Specific techniques can help reduce bias at each stage of a recommender system. Reducing bias in our recommender systems is crucial to successfully recommending dream jobs to hundreds of millions members worldwide, while being true to LinkedIn's vision: "To create economic opportunity for every member of the global workforce".