Recommending Dream Jobs in a Biased Real World

TL;DR

This paper discusses how biases in data and modeling affect recommender systems, especially in job recommendations, and explores techniques to mitigate these biases to promote fair and accurate recommendations.

Contribution

It highlights the impact of biases at different stages of recommender systems and reviews techniques to reduce bias, aiming to improve fairness in job recommendations.

Findings

Biases affect offline training, evaluation, and online serving.

Bias reduction techniques can improve recommendation fairness.

Addressing biases supports equitable job opportunities.

Abstract

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".