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Anomaly Detection Through Explanations

Speaker Name: 
Leilani Gilpin
Speaker Title: 
Research Scientist
Speaker Organization: 
Sony AI
Start Time: 
Monday, March 1, 2021 - 11:00am
End Time: 
Monday, March 1, 2021 - 12:15pm
Via Zoom Link
Lise Getoor



Under most conditions, complex systems are imperfect.  When errors occur, as they inevitably will, systems need to be able to (1) localize the error and (2) take appropriate action to mitigate the repercussions of that error.  In this talk, I present new methodologies for detecting and explaining errors in complex systems. My novel contribution is a system-wide monitoring architecture, which is composed of introspective, overlapping committees of subsystems. Each subsystem is encapsulated in a "reasonableness" monitor, an adaptable framework that supplements local decisions with  commonsense data and reasonableness rules.  This framework is dynamic and introspective: it allows each subsystem to defend its decisions in different contexts: to the committees it participates in and to itself.  For reconciling system-wide errors, I developed a comprehensive architecture: "Anomaly Detection through Explanations (ADE)."  The ADE architecture contributes an explanation synthesizer that produces an argument tree, which in turn can be traced and queried to determine the support of a decision, and to construct counterfactual explanations.  I have applied this methodology to
detect incorrect labels in semi-autonomous vehicle data, and to reconcile inconsistencies in simulated, anomalous driving scenarios.

My work has opened up the new area of explanatory anomaly detection, towards a vision in which: complex machines will be articulate by design; dynamic, internal explanations will be part of the design
criteria, and system-level explanations will be able to be challenged in an adversarial proceeding.



Leilani H. Gilpin is a research scientist at Sony AI and a collaborating researcher at MIT CSAIL.  Her research focuses on enabling opaque autonomous systems to explain themselves for robust decision-making, system debugging, and accountability.  Her current work integrates explainability into reinforcement learning for game-playing agents. She received her PhD in Electrical Engineering and Computer Science from MIT in 2020, and holds an M.S. in Computational and Mathematical Engineering from Stanford University, and a B.S. in Mathematics (with honors), B.S. in Computer Science (with highest honors), and a music minor from UC San Diego.  Outside of research, Leilani enjoys
swimming, cooking, and rowing.


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