Potential Abstract: This research article presents a novel approach for real-time representation learning of naive prejudices using distributed hashing techniques. In the field of education, understanding and addressing prejudices is crucial for creating inclusive and equitable learning environments. However, traditional methods for identifying and mitigating prejudices often rely on labor-intensive manual processes or static representations that may not capture the dynamic nature of biases. To address this challenge, we propose a real-time framework that leverages distributed hashing to continuously learn and update representations of naive prejudices based on incoming data streams. By utilizing distributed hashing, we are able to efficiently process large volumes of data and adapt our models to changing biases in a timely manner. Our experimental results demonstrate the effectiveness of our approach in not only accurately capturing naive prejudices in real time, but also in providing interpretable insights that can inform targeted interventions to promote diversity and inclusion in educational settings.
Potential References:
- Representation learning of temporal dynamics for skeleton-based action recognition
- Kicking Prejudice: Large Language Models for Racism Classification in Football Discourse on Social Media
- Kicking Prejudice: Large Language Models for Racism Classification in Soccer Discourse on Social Media
- Max-margin deepwalk: Discriminative learning of network representation.
- Contrastive representation learning: A framework and review
