The Persuasive Effect of Privacy Recommendations

Several researchers have recently suggested that in order to avoid privacy problems, location-sharing services should provide finer-grained methods of location-sharing. This may however turn each “check-in” into a rather complex decision that puts an unnecessary burden on the user. We present two studies that explore ways to help users with such location-sharing decisions. Study 1 shows that users’ evaluation of their activity is a good predictor of the sharing action they choose. Study 2 develops several “privacy recommenders” that tailor the list of sharing actions to this activity evaluation. We find that these recommenders have a strong persuasive effect, and that users find short lists of recommended actions helpful. We also find, however, that users ultimately find it more satisfying if we do not ask them to evaluate the activity

Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond

Joint intent detection and slot filling, which is also termed as joint NLU (Natural Language Understanding) is invaluable for smart voice assistants. Recent advancements in this area have been heavily focusing on improving accuracy using various techniques. Explainability is undoubtedly an important aspect for deep learning-based models including joint NLU models. Without explainability, their decisions are opaque to the outside world and hence, have tendency to lack user trust. Therefore to bridge this gap, we transform the full joint NLU model to be `inherently' explainable at granular levels without compromising on accuracy. Further, as we enable the full joint NLU model explainable, we show that our extension can be successfully used in other general classification tasks. We demonstrate this using sentiment analysis and named entity recognition.Comment: Accepted at CIKM 202