Update.
Follow up reports almost always show the hack was larger than
originally reported. Why would a camera provider have all this
information?
Report:
CBP contractor hack was vast, revealed plans for border surveillance
A
cyberattack
on
a subcontractor for U.S.
Customs and Border Protection (CBP)
exposed surveillance plans and much more than was previously
disclosed, according to a new report.
Earlier
this month, U.S. Customs and Border Protection said photos of
travelers and license plates had been compromised
during a cyberattack,
adding that less than 100,000 people were affected.
However,
the Washington
Post reported
on Friday that the cyberattack also compromised documents including
“detailed schematics, confidential agreements, equipment lists,
budget spreadsheets, internal photos and hardware blueprints for
security systems.”
… The
available information taken was “hundreds of gigabytes,” the
newspaper reported.
No
standard definition of ‘fairness?’
How
AI Can Help with the Detection of Financial Crimes
According
to Dickie, AI can have a significant impact in data-rich domains
where prediction and pattern recognition play an important role. For
instance, in areas such as risk assessment and fraud detection in the
banking sector, AI can identify aberrations by analyzing past
behaviors. But, of course, there are also concerns around issues
such as fairness, interpretability, security and privacy.
It
gets complicated fast…
An Analysis
of the Consequences of the General Data Protection Regulation on
Social Network Research
This article examines the principles outlined in
the General Data Protection Regulation (GDPR) in the context of
social network data. We
provide both a practical guide to GDPR-compliant social network data
processing, covering aspects such as data collection,
consent, anonymization and data analysis, and a broader discussion of
the problems emerging when the general principles on which the
regulation is based are instantiated to this research area.
Why did you do that, Mr. Terminator?
TED:
Teaching AI to Explain its Decisions
Artificial intelligence systems are being
increasingly deployed due to their potential to increase the
efficiency, scale, consistency, fairness, and accuracy of decisions.
However, as many of these systems are opaque in their operation,
there is a growing demand for such systems to provide explanations
for their decisions. Conventional approaches to this problem attempt
to expose or discover the inner workings of a machine learning model
with the hope that the resulting explanations will be meaningful to
the consumer. In contrast, this paper suggests a new approach to
this problem.
Businesses exist to take risks. Lawyers exist to
avoid risks?
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