Title: Interactive and Explainable Machine Learning For Humans

Date: April 18, 2022

Time: 12:00 – 2:00 PM Eastern Time

Location: Remote -- 

https://gatech.zoom.us/j/92102254383?pwd=ZFhkblZFbnVJcThXMDgzRXBnajFBUT09

Andrew Silva

Ph.D. Student in Computer Science

School of Interactive Computing

Georgia Institute of Technology

Committee:

Dr. Matthew Gombolay (Advisor), School of Interactive Computing, Georgia Institute of Technology

Dr. Sonia Chernova, School of Interactive Computing, Georgia Institute of Technology

Dr. Mark Riedl, School of Interactive Computing, Georgia Institute of Technology

Dr. Diyi Yang, School of Interactive Computing, Georgia Institute of Technology

Dr. Barry Theobald, Apple, Inc.

Abstract:

Interactivity and explainability within machine learning present an opportunity to improve human perceptions of AI, improve AI performance in complex domains, and improve human-AI teaming. While conventional machine learning research has focused on optimizing solely for an objective function (i.e., maximizing task completion rates, accuracy, or word-likelihood), virtual or embodied agents that are designed to interface with humans must be interactive and offer transparency to end-users. As machine learning is deployed in the real world, from smartphones to household robots, human users will benefit from personal- ization in their lives (e.g., predicting next words while considering an individual’s vernacular, or executing household tasks according to an individual user’s home layout or preference). Further, such agents must offer explanations and insights into their decision-making and behavior to improve human trust, understanding, and usability of such systems, and even to satisfy legal requirements. This thesis will present novel approaches to interaction and personalization in machine learning while simultaneously offering improved explainability for such systems. The result of this work will be the empowerment of human users to both (1) better-understand machine learning agents, and (2) tailor machine learning systems to their individual preferences.