CAREER: Interpretable and Robust Machine Learning Models: Analysis and Algorithms

While the impact of machine learning continues to increase in different areas---from recommendation systems to algorithmic trading, and from medical imaging diagnosis to molecular biology---some limitations of these increasingly complex models represent important shortcomings for their safe and responsible deployment. One of these limitations is the lack of interpretability of these predictors, making it difficult to faithfully determine the role of the most relevant portions of a given input in producing a certain output. Another limitation is their brittleness, as these outputs can also be remarkably unstable even to very small perturbations of the inputs. These problems can compromise the safe deployment of modern machine learning tools in sensitive domains, such as medical imaging. This project will develop formal methods and algorithms to alleviate these shortcomings. This CAREER project will develop a general framework to interpret complex predictors in a robust and certifiable manner. In particular, this project will first define new notions of local feature importance and develop correct and efficient methods to estimate them. These definitions will be given in terms of local conditional independence tests while making minimal assumptions about the prediction functions, as well as extensions to semantically important concepts. Second, this project will propose and analyze algorithms to certify the stability of predictive models locally on manifolds, as well as guarantee the stability and robustness of model interpretations. The methods derived from this project will be evaluated on a series of medical imaging problems that include chest X-rays and computed tomography. In addition, this project will carry out a holistic educational and outreach program, including K-12 and community outreach through the Johns Hopkins Center for Educational Outreach, strategic undergraduate and graduate research projects, and broad dissemination to both the scientific community and the general public, among other initiatives. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.