fairness-in-ai

This paper demonstrates that causal fairness mechanisms, specifically edge cuts on causal graphs, are portable across various synthetic data generator families including GANs and diffusion models, with minimal impact on data fidelity and utility.

The paper introduces OBJECTION, an inference-time pipeline using adversarial lawyer agents to mitigate guilty bias in legal judgment prediction models, demonstrating a significant reduction in false guilty rates and releasing a new 'Natural Innocent' dataset.