Towards Trustworthy Representation Learning

Abstract

Representation learning (RL) aims to extract latent features from various types of data and then facilitate a wide range of downstream data analytics tasks, such as classification, clustering, outlier detection, recommender systems, etc. Prior efforts on RL in the past decades mainly focus on developing models to largely retain useful information (e.g., discriminative patterns, semantic knowledge) from data while discard redundant and noisy information. In the data mining and machine learning communities, some recent efforts attempt to encourage both task-oriented performance and trustworthiness of the model, suggesting a pathway towards trustworthy representation learning (TRL). Although trustworthiness has been increasingly discussed from different perspectives (e.g., fairness, explainability, robustness), the intertwined connections between representation learning and trustworthiness have not been formally discussed and clearly revealed yet. In this Blue Sky vision paper, for the first time, we present a conceptual framework that illustrates how to characterize trustworthiness in representation learning, discuss the research challenges, and point out future research opportunities.

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