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
Evaluating the diversity and utility of materials proposed by generative models
Alexander New, Michael Pekala, Elizabeth A. Pogue, Nam Q. Le, Janna Domenico, Christine D. Piatko, Christopher D. Stiles
Analyzing and controlling diversity in quantum-behaved particle swarm optimization
Li-Wei Li, Jun Sun, Chao Li, Wei Fang, Vasile Palade, Xiao-Jun Wu
Diversity is Strength: Mastering Football Full Game with Interactive Reinforcement Learning of Multiple AIs
Chenglu Sun, Shuo Shen, Sijia Xu, Weidong Zhang
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander Ratner, Ranjay Krishna, Jiaming Shen, Chao Zhang