Mirroring to Build Trust in Digital Assistants — these experiments are designed to measure whether users prefer and trust an assistant whose conversational style matches their own. To this end, we conducted a user study where subjects interacted with a digital assistant that responded in a way that either matched their conversational style, or did not. Using self-reported personality attributes and subjects’ feedback on the interactions, we built models that can reliably predict a user’s preferred conversational style.
DCC SEND startkeylogger — my new favorite example of why you have to be aware of the cost of false positives, not just of false negatives. Most people can see the sense in, “it’d be bad to let a command-and-control server talk to an infected machine,” and can’t see the risk in, “so kill any connection that looks like it’s a C&C server talking to an infected machine”.