Sure, cut the fluff. Use knowledge distillation and parameter pruning to keep only the bits that actually drive decision‑making. Then bias‑correct the small model with a calibrated loss that’s monitored on a separate fairness dataset. The trick is to iterate fast: test, measure bias, adjust, repeat. Skip the huge ensemble that just adds noise. Keep the pipeline tight, metrics tight, and cut the rest.