Diversity Awareness
Diversity awareness in artificial intelligence focuses on mitigating biases and enhancing the fairness and inclusivity of AI systems by addressing the underrepresentation of diverse populations in data and models. Current research emphasizes developing metrics to quantify diversity in synthetic datasets and generated outputs, employing techniques like contrastive learning and diffusion models to improve diversity in generated content, and adapting large language models to better represent diverse linguistic and cultural contexts. This work is crucial for ensuring the responsible development and deployment of AI, preventing discriminatory outcomes, and promoting equitable access to AI benefits across diverse populations.
Papers
Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy
Min Zeng, Caiquan Liu, Shiqi Zhang, Li Xie, Chen Sang, Xiaoxin Chen, Xiaoxin Chen
CONDEN-FI: Consistency and Diversity Learning-based Multi-View Unsupervised Feature and In-stance Co-Selection
Yanyong Huang, Yuxin Cai, Dongjie Wang, Xiuwen Yi, Tianrui Li
SVGDreamer++: Advancing Editability and Diversity in Text-Guided SVG Generation
Ximing Xing, Qian Yu, Chuang Wang, Haitao Zhou, Jing Zhang, Dong Xu
DiffSLT: Enhancing Diversity in Sign Language Translation via Diffusion Model
JiHwan Moon, Jihoon Park, Jungeun Kim, Jongseong Bae, Hyeongwoo Jeon, Ha Young Kim