Unfairness towards subjective opinions in Machine Learning
This addresses fairness issues in ML applications, particularly for users affected by opinion exclusion, but appears incremental as it builds on existing fairness concepts.
The paper tackles the problem of unfairness in machine learning systems due to the exclusion of subjective opinions, formalizing this as a new notion of unfairness and proposing methods to quantify and visualize its causes.
Despite the high interest for Machine Learning (ML) in academia and industry, many issues related to the application of ML to real-life problems are yet to be addressed. Here we put forward one limitation which arises from a lack of adaptation of ML models and datasets to specific applications. We formalise a new notion of unfairness as exclusion of opinions. We propose ways to quantify this unfairness, and aid understanding its causes through visualisation. These insights into the functioning of ML-based systems hint at methods to mitigate unfairness.