Saturday, June 22, 2019


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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