Bettina Berendt

TL;DR: An experiment in which self-reported privacy preferences of 171 participants were compared with their actual disclosing behavior during an online shopping episode, suggesting that current approaches to protect online users' privacy may face difficulties to do so effectively.

TL;DR: The possibility of a "transparent human," whose vital information is up for grabs, can most easily be envisioned in the realm of e-commerce, due in part to the large amounts of data available, and the high payoffs expected from using this data for marketing purposes.

TL;DR: In this article, the authors conducted an experiment in which they compared self-reported privacy preferences of 171 participants with their actual disclosing behavior during an online shopping episode, and they found that regardless of their specific privacy concerns, most participants did not live up to their self reported privacy preferences.

TL;DR: A set of performance measures that are sensitive to two types of reconstruction errors and appropriate for different applications in knowledge discovery (KDD) applications are proposed that help the analyst in the selection of the heuristic best suited for the application at hand.

TL;DR: A broad multidisciplinary overview of the area of bias in AI systems is provided, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well‐grounded in a legal frame.