What happens with steering vectors to remove biases?

Getting back in the safety field after 4 years feels great. I had a paper on concept distillation where I showed how steering vectors based method did'nt help to debias model since the concepts itself are understood wrongly [ Concept distillation , NeurIPS 2023]. I am so happy to see the steering vectors work picked up in interpretability and was curious to see how methods like ActAdd would work in this debiasing setting? So, I apply ActAdd to debias a model. Consider Colored-MNIST setting (age-old) where a model is trained on digits in a specific color always (say it sees zeros always in red). What would you expect to happen? It learns to associate digit with a specific color (sees red, says zero)! Now when such model is tested on reversed color digits, it fails badly (nearly zero accuracy). Classifier learns to associate color with digits during training; in test, gets zero accuracy. How to debias this model using steering vectors? We can find directions of color red or simply color and steer model activations away from this direction simply. Lets try it out. Results (will be discussed one by one below) Method Mean Test Accuracy (3 seeds) Base model 0.0 Base model on grayscale in