A system for removing a concept from a trained neural network for executing a classification task on an input corrupted data item , wherein the input corrupted data item is an image or video, and an output of the classification task is a classification of an object present in the image or video, the system comprising: the trained neural network , wherein the trained neural network comprises a hidden layer ; and a plurality of classifiers respectively incorporated in a plurality of layers of the hidden layer , wherein: the hidden layer comprises a plurality of contracting layers and respectively a plurality of layers preceding the contracting layers , wherein the plurality of contracting layers have smaller dimensions than the plurality of layers preceding the contracting layers ; the plurality of classifiers are respectively incorporated in the plurality of layers preceding the contracting layers ; each classifier defines a representation vector at the layer of the hidden layer , wherein the concept is relevant to the classification task and the representation vector classifies instances of the concept and non-instances of the concept at the layer; each classifier defines a concept activation vector , wherein the concept activation vector is a normal vector to the representation vector and the concept activation vector comprises an adversarial penalty objective to reduce the instances of the concept at the layer; and a loss function of the trained neural network is optimised based on a downstream loss of the classification task and the adversarial penalty objective. the trained neural network , wherein the trained neural network comprises a hidden layer ; and a plurality of classifiers respectively incorporated in a plurality of layers of the hidden layer , wherein: the hidden layer comprises a plurality of contracting layers and respectively a plurality of layers preceding the contracting layers , wherein the plurality of contracting layers have smaller dimensions than the plurality of layers preceding the contracting layers ; the plurality of classifiers are respectively incorporated in the plurality of layers preceding the contracting layers ; each classifier defines a representation vector at the layer of the hidden layer , wherein the concept is relevant to the classification task and the representation vector classifies instances of the concept and non-instances of the concept at the layer; each classifier defines a concept activation vector , wherein the concept activation vector is a normal vector to the representation vector and the concept activation vector comprises an adversarial penalty objective to reduce the instances of the concept at the layer; and a loss function of the trained neural network is optimised based on a downstream loss of the classification task and the adversarial penalty objective. the hidden layer comprises a plurality of contracting layers and respectively a plurality of layers preceding the contracting layers , wherein the plurality of contracting layers have smaller dimensions than the plurality of layers preceding the contracting layers ; the plurality of classifiers are respectively incorporated in the plurality of layers preceding the contracting layers ; each classifier defines a representation vector at the layer of the hidden layer , wherein the concept is relevant to the classification task and the representation vector classifies instances of the concept and non-instances of the concept at the layer; each classifier defines a concept activation vector , wherein the concept activation vector is a normal vector to the representation vector and the concept activation vector comprises an adversarial penalty objective to reduce the instances of the concept at the layer; and a loss function of the trained neural network is optimised based on a downstream loss of the classification task and the adversarial penalty objective. <EOS>