How Can You Trust Machine Learning?


Machine learning (ML) and AI systems are becoming integral parts of every aspect of our lives. The definition, development and deployment of these systems are driven by (complex) human choices. And, as these AIs are making more and more decisions for us, and the underlying ML systems are becoming more and more complex, it is natural to ask the question: "How can we trust machine learning?"
In this talk, I'll present a framework, anchored on three pillars: Clarity, Competence and Alignment. For each, I'll describe algorithmic and human processes that can help drive towards more effective, impactful and trustworthy AIs. For Clarity, I'll cover methods for making the predictions of machine learning more explainable. For Competence, I will focus on methods to evaluating and testing ML models with the rigor that we apply to complex software products. Finally, for Alignment, I'll describe the complexities of aligning the behaviors of an AI with the values we want to reflect in the world, along with methods that can yield more aligned outcomes.
Through this discussion, we will cover both fundamental concepts and actionable algorithms and tools that can lead to increased trust in ML.


Carlos Guestrin is a Professor in the Computer Science Department at Stanford University. His previous positions include the Amazon Professor of Machine Learning at the Computer Science & Engineering Department of the University of Washington, the Finmeccanica Associate Professor at Carnegie Mellon University, and the Senior Director of Machine Learning and AI at Apple, after the acquisition of Turi, Inc. (formerly GraphLab and Dato) — Carlos co-founded Turi, which developed a platform for developers and data scientist to build and deploy intelligent applications. He is a technical advisor for His team also released a number of popular open-source projects, including XGBoost, LIME, Apache TVM, MXNet, Turi Create, GraphLab/PowerGraph, SFrame, and GraphChi.

Carlos received the IJCAI Computers and Thought Award and the Presidential Early Career Award for Scientists and Engineers (PECASE). He is also a recipient of the ONR Young Investigator Award, NSF Career Award, Alfred P. Sloan Fellowship, and IBM Faculty Fellowship, and was named one of the 2008 ‘Brilliant 10’ by Popular Science Magazine. Carlos’ work received awards at a number of conferences and journals, including ACL, AISTATS, ICML, IPSN, JAIR, JWRPM, KDD, NeurIPS, UAI, and VLDB. He is a former member of the Information Sciences and Technology (ISAT) advisory group for DARPA.

Open Data Science




Open Data Science
One Broadway
Cambridge, MA 02142

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google