The Lockheed Corp. (Calabasas, CA) and AT&T (New York, NY) have signed an agreement to jointly develop and market intelligent transportation systems. The two companies are responding to the Intermodal Surface Transportation Act of 1991, which calls for enhancing roadway capacity, safety, efficiency, and air quality through the development of intelligent vehicle highway systems. Electronic toll collection systems, traffic management systems, in-car navigational and route planning systems are among the systems being developed. UKbased Empires Stores, a mail order company, has reduced the clerical work in its credit department by about 30%, thanks to the implementation of an intelligent system. The company has successfully automated the decision-making process for passing or rejecting orders referred by its performance scoring system.
I'll save you the suspense. According to a sneak peak of a new report from LendingTree (full report coming soon), users of Apple Mac computers have the highest credit scores both in the personal loan and mortgage loan categories. The report also looked at purchase requests by device (the percent of the one million loan dataset in each category) and the average final loan amount. You'll either be delightfully surprised or moderately ambivalent by the results. Google Android users not only had the lowest overall credit score in the personal loan category, but in the mortgage category as well.
Algorithmic systems (such as those deciding mortgage applications, or sentencing decisions) can be very difficult to understand, for experts as well as the general public. The EU General Data Protection Regulation (GDPR) has sparked much discussion about the "right to explanation" for the algorithm-supported decisions made about us in our everyday lives. While there's an obvious need for transparency in the automated decisions that are increasingly being made in areas like policing, education, healthcare and recruitment, explaining how these complex algorithmic decision-making systems arrive at any particular decision is a technically challenging problem--to put it mildly. In their article "Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR" which is forthcoming in the Harvard Journal of Law & Technology, Sandra Wachter, Brent Mittelstadt, and Chris Russell present the concept of "unconditional counterfactual explanations" as a novel type of explanation of automated decisions that could address many of these challenges. Counterfactual explanations describe the minimum conditions that would have led to an alternative decision (e.g. a bank loan being approved), without the need to describe the full logic of the algorithm.
It can be difficult to design and develop artificial intelligence systems to meet specific quality standards. Often, AI systems are designed to be "as good as possible" rather than meeting particular targets. Using the Design for Six Sigma quality methodology, an automated insurance underwriting expert system was designed, developed, and fielded. Using this methodology resulted in meeting the high quality expectations required for deployment.