Causal Bias Detection in Generative Artifical Intelligence

arXiv:2605.15217v1 Announce Type: new Abstract: Instruction-tuned language models exhibit behavioural fairness in high-stakes decisions while retaining biased associations in

arXiv:2605.00012v1 Announce Type: cross Abstract: Modern large language models (LLMs) are used in many business applications in general, and specifically in web search systems and

arXiv:2604.21036v1 Announce Type: new Abstract: Text-to-image(T2I) models like Stable Diffusion and DALL-E have made generative AI widely accessible, yet recent studies reveal

arXiv:2603.18729v1 Announce Type: new Abstract: Many works in the literature show that LLM outputs exhibit discriminatory behaviour, triggering stereotype-based inferences based

arXiv:2609.19944v1 Announce Type: new Abstract: Large language models (LLMs) have been applied to causal discovery, but candidate-graph generation rarely treats premature omission

arXiv:2604.09923v1 Announce Type: new Abstract: Text-to-image (T2I) models, and their encoded biases, increasingly shape the visual media the public encounters. While researchers

arXiv:2605.21758v1 Announce Type: new Abstract: Explainable AI (XAI) methods identify which features are relevant to a model's predictions but often fail to clarify why certain

arXiv:2604.22089v1 Announce Type: cross Abstract: Generative Artificial Intelligence (GAI) systems that can automatically generate content in the form of source code or other