Theoretical Foundations for Safe, Adaptive and Interpretable Machine Learning

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The proposed research is in the area of developing theoretical foundations to machine learning. Machine learning technologies are being used in various every-day life systems with an ever growing influence on industry, science and society. This includes commercial usage such as in recommendation systems or personal devices, but also critical applications such as autonomous vehicles, brain-machine interfaces or medical assistance. As machine learning practice currently enjoys enormous success and learning methods are employed in a vast number of application domains, it becomes increasingly crucial to develop solid and formal analysis and understanding of these methods. Many of the currently most successful learning methods (in particular deep learning based ones) have a “blackbox” nature. While they are becoming enormously successful at building accurate predictors based on large amounts of data, there is a growing societal sense of discomfort about data-driven tools affecting people's lives in ever expanding, yet uncontrolled, ways. We have, in fact, distressingly little understanding on why and how these methods work, what influences their outcomes, what type of correctness guarantees one can provide for their outcomes, and how we may interpret and communicate decisions made by a machine learned system to non-experts. The long-term goal of the proposed research program is to develop a better formal understanding of requirements and methods for safe and interpretable machine learning. To start addressing this challenge, over the next few years, we plan to explore the potential of interactive machine learning for this purpose. So far, research in interactive learning has mostly focused on how to design active label queries to efficiently achieve high statistical accuracy in supervised learning tasks. Here we aim to explore the potential of active feedback loops in learning process for three other objectives: (1) Safety as machine learning technologies enter critical applications, such as self-driving cars or medical devices, we need stronger guarantees of correct behavior than the statistical guarantees we currently rely on. (2) Adaptability machine learning systems are often trained in environments that are different from the one they are employed in. Thus, we need ways to let the system adapt to such changes. (3) Interpretability as machine learned systems are used for decision support directly affecting human individuals such as in the financial sector or in the judicial system, we need to develop ways to make these decisions comprehensible by humans. To this aim, we plan to develop novel protocols of user interaction with a learning system. Developing such from a foundational perspective will help building bridges between this fast developing technology and non-experts who are using or who are affected by machine learning.