Analyzing and Preventing Bias in Text Based Personal Trait Prediction Algorithms
Personality prediction based on textual data is one topic gaining attention recently for its potential in large scale personalized applications such as social media based marketing and political campaigning. However, when applying this technology in real world applications, users often encounter situations in which the personality traits derived from different sources (e.g. social media posts versus emails) are inconsistent. Varying results for the same individual renders the tool ineffective and hard to trust. This paper demonstrates the impact of domain bias in automated text based personality prediction, and proposes a novel method to correct domain bias. The proposed approach is generic since it requires neither retraining the system using examples from an application domain, nor any knowledge of the original training data used by a personal trait analysis tool. We conduct comprehensive experiments to evaluate the effectiveness of the method, and the findings indicate a significant improvement of prediction accuracy (e.g. a 20 30% relative error reduction) with the proposed method.
2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)