Oct 4, 2021
In this episode, we will continue with part two of a four-part
series looking at Responsible AI (Listen to part one:
Alternative Data in Risk Modeling).
One of the major challenges with effectively developing, deploying,
and managing AI systems are often related to the “black box” nature
of the model. Specifically, the complexity and non-linear nature of
variables in some black-box AI models may be difficult to explain
or understand. This includes explainability of the model logic as
well as the individual decisions made by the model. In addition,
the relative lack of transparency challenges model development and
model validation teams to foresee unintended consequences from
model usage, which could create an operational risk if the model is
implemented in production.
Speakers
Iain Brown, Ph.D., Head of Data Science, SAS
UK&I
Matthew Jones, Ph.D., Head of Retail Decision
Modelling, Risk Community, Nationwide Building Society
Moderator:
Lisa Ponti, Ph.D., Vice President, Educational
Outreach, Global Association of Risk Professionals (GARP)
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Over the years, GARP and SAS have worked together to bring risk
practitioners unique insights on a variety of topics related to
financial risk and have partnered on this episode of our COVID
podcast series.
About
SAS
As a leader in analytics, SAS has more than 40 years of experience
helping organizations solve their toughest problems. Our
unrelenting commitment to innovation enables banks to modernize and
sustain a competitive edge. SAS provides an integrated,
enterprise-wide risk-management platform for managing risk in an
organization, from strategic to reputational, operational,
financial or compliance-related risk management. Learn more about
how SAS is driving innovation and business value for risk and
finance professionals at www.sas.com/risk.