Anomaly Detection through Explanations


Event Speaker: 

Leilani Gilpin

Event Location: 

via Zoom, see details below

Event Date/Time: 

Thursday, June 11, 2020 - 3:00pm


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
complex system failures.  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 that I call "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,
working towards a vision in which complex systems will be articulate
by design: they will be dynamic; internal explanations will be part of
the design criteria; system-level explanations will be provided, and
they can be challenged in an adversarial proceeding.
Thesis Supervisor: Prof. Gerald Sussman
To attend this defense, please contact the candidate at lgilpin at mit dot edu