Self-adaptive Artificial Intelligence

Machine learning tools, like deep neural networks, are perceived to be black boxes. That is, the only way of changing their internal data models is to retrain these models using different inputs. This is ineffective in dynamic systems that are prone to changes, like concept drift. A new promising solution is transparent artificial intelligence, based on the notions of interpretation and explanation, whose objective is to correlate the internal data models with predictions. The research question being addressed is whether we can have a self-adaptive machine learning system that is able to interpret and explain its data model in order for it to be controlled. In this position paper, we present our initial thoughts whether this can be achieved.