English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Algorithms engineered to leverage rich behavioral and biometric data topredict individual attributes and actions continue to permeate public andprivate life. A fundamental risk may emerge from misconceptions about thesensitivity of such data, as well as the agency of individuals to protect theirprivacy when fine-grained (and possibly involuntary) behavior is tracked. Inthis work, we examine how individuals adjust their behavior when incentivizedto avoid the algorithmic prediction of their intent. We present results from avirtual reality task in which gaze, movement, and other physiological signalsare tracked. Participants are asked to decide which card to select without analgorithmic adversary anticipating their choice. We find that whileparticipants use a variety of strategies, data collected remains highlypredictive of choice (80% accuracy). Additionally, a significant portion ofparticipants became more predictable despite efforts to obfuscate, possiblyindicating mistaken priors about the dynamics of algorithmic prediction.

Covert Embodied Choice: Decision-Making and the Limits of Privacy Under Biometric Surveillance