Speaker: Chris Biow, SVP, Global Public Sector, Basis Technology
In government operations, we are invariably dealing with people. While we sometimes have strong identifiers such as numbers or biometrics, more commonly we deal with our people by their names, at least at first. Yet names can be confusing and ambiguous, varying infinitely across languages, alphabets, cultures, scripts, documents, and automated systems. Whether our function is citizen service or national security, it is essential we know with whom we are dealing with, regardless of these variations. We must associate name variants without falsely confusing people with others of similar names. We’ll give specific examples of how this can be a deadly serious issue: false positives have infringed civil liberties, and a false negative resulted in terrorist mayhem at the Boston Marathon.
Fortunately, Natural Language Processing, including some of the most recent Artificial Intelligence (AI) techniques, can reduce these risks. We will explore how name-matching and identity resolution, driven by Machine Learning, provides transparency, showing exactly why names do or do not match. Mission applications will include national health care enrollment, Anti-Money Laundering, and border enforcement of watch-lists and denied-entry orders.
Chris Biow leads the Global Public Sector team at Basis Technology, working with government customers to meet their text analytic mission needs using Basis software and services. After flying with the US Navy as an F-14 Tomcat RIO (Radar Intercept Officer), Chris founded a sales-enablement software company and then worked delivering Public Sector solutions with search and database software at Verity, Autonomy, as Federal CTO at MarkLogic and at MongoDB. Throughout this time, he has specialized in large textual data problems as applied to anticipatory intelligence.
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