Casual Conversation

Casual conversation datasets, like Casual Conversations v2, are increasingly used to study algorithmic bias in computer vision and speech recognition models. Research focuses on evaluating model performance across diverse demographic attributes (age, gender, skin tone) to identify and mitigate biases, often employing large language models and transformer architectures. These efforts are crucial for developing fairer and more robust AI systems, impacting both the scientific understanding of algorithmic bias and the practical deployment of AI in real-world applications.