Interpretable Machine Learning
Valerie Chen, Jeffrey Li, Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Abstract
The emergence of machine learning as a society-changing technology in the past decade has triggered concerns about people's inability to understand the reasoning of increasingly complex models. The field of IML (interpretable machine learning) grew out of these concerns, with the goal of empowering various stakeholders to tackle use cases, such as building trust in models, performing model debugging, and generally informing real human decision-making.
Topics & Concepts
DebuggingComputer scienceField (mathematics)Artificial intelligenceMachine learningData scienceKnowledge managementHuman–computer interactionSoftware engineeringProgramming languagePure mathematicsMathematicsExplainable Artificial Intelligence (XAI)Adversarial Robustness in Machine LearningMachine Learning and Data Classification