Can We Ever Fully Remove Bias from AI Recommendation Systems?

This study explores gender bias in latent factor recommendation (LFR) models for podcast suggestions. Even after removing gender as a feature, significant attribute association bias persisted—showing that bias is deeply embedded within the model’s latent space. The findings echo prior research suggesting that feature removal offers only “superficial” fixes. True mitigation requires auditing and monitoring implicit bias over time, as systematic gender associations continue to influence how AI serves and reinforces user behaviors.