GLEaN: A Text-to-image Bias Detection Approach for Public Comprehension

arXiv:2605.15208v1 Announce Type: cross Abstract: Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for

arXiv:2607.03397v1 Announce Type: new Abstract: Text-to-Image diffusion models often propagate harmful bias inherited from the training data. Existing bias mitigation techniques

arXiv:2605.11365v1 Announce Type: new Abstract: Automated systems built on artificial intelligence (AI) are increasingly deployed across high-stakes domains, raising critical

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

arXiv:2604.26233v1 Announce Type: new Abstract: As Large Language Models (LLMs) are proposed as legal decision assistants, and even first-instance decision-makers, across a range

arXiv:2603.18015v1 Announce Type: cross Abstract: Although automated harmful content detection systems are frequently used to monitor online platforms, moderators and end users