Ethics and Society Newsletter #4: Bias in Text-to-Image Models

The newsletter discusses the prevalence of bias in text-to-image models, highlighting recent studies that quantify biases in datasets and the generated outputs. It emphasizes the need for improved fairness and representational accuracy in model training and evaluation. This is crucial for practitioners as it underscores the importance of dataset curation and model evaluation metrics to mitigate bias in AI applications.