State Representation Learning for Control: An Overview <s> Providing interpretable systems <s> We summarize the potential impact that the European Union’s new General Data Protection Regulation will have on the routine use of machine learning algorithms. Slated to take effect as law across the EU in 2018, it will restrict automated individual decision-making (that is, algorithms that make decisions based on user-level predictors) which “significantly affect” users. The law will also effectively create a “right to explanation,” whereby a user can ask for an explanation of an algorithmic decision that was made about them. We argue that while this law will pose large challenges for industry, it highlights opportunities for computer scientists to take the lead in designing algorithms and evaluation frameworks which avoid discrimination and enable explanation. <s> BIB001 </s> State Representation Learning for Control: An Overview <s> Providing interpretable systems <s> Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models. <s> BIB002
In 2018, European Union regulations on algorithmic decision-making include a "right to explanation", "right to opt-out" and "non discrimination" of models BIB001 . Artificial intelligence research is thus granted with an opportunity to further provide meaningful explanations to why algorithms work the way they do. The interpretability of results in machine learning is however a challenging problem that needs proper definition  . In any case, monitoring the degree to which AI systems show the same thinking flows as humans is invaluable and crucial; not only to explain how human cognition works, but also to help AI make better and more fair decisions BIB002 . We define interpretability in the SRL context as the capacity for a human to be able to link a variation in the representation to a variation in the environment, and be able to know why the representation was sensitive to this variation. As SRL is designed to be able to give this level of interpretability, it could help improving the understanding we have about learning algorithms' output. Indeed, the higher the dimension, the less interpretable the result is for humans. Therefore, the dimensionality reduction induced by SRL, coupled with the link to the control and possible disentanglement of variation factors, could be highly beneficial to improve our understanding capacity of the decisions made by algorithms using this state representation.