1. Pusan lok'ŏllit'i wa lok'ŏl munhwa yulli -- 2. Han'guk lok'ŏllit'i wa lok'ŏl munhwa yulli -- 3. Asia lok'ŏllit'i wa lok'ŏl munhwa yulli.

Ch'aek mŏri e -- 1. Ch'oe Han-gi ŭi sam kwa kŭ ŭi sidae -- 2. Silchŭng kwa siryong ŭi sirhakchŏk hangmun'gwan -- 3. Chujahak pip'an kwa Kihak ŭi hyŏngsŏng -- 4. Sŏyang kwahak kisul ŭi suyong kwa Kihakchŏk pyŏnyong -- 5. Insingnon ŭi chŏnhwan kwa kaekkwan insik ŭi pangbŏmnon -- 6. T'al Chujahakchŏk sayu ŭi chŏn'gae wa segye p'yŏnghwaron -- Kyŏllon : Chosŏn hugi Sirhak kwa Ch'oe Han-gi ŭi ch'ŏrhak.

This study analyzes the increasing presence and capabilities of wearable computing devices in the cornucopia of personalized digital data. We argue that the institutional data practices typical of Google Glass will pose policy challenges and herald yet another dramatic shift to personalized data marketing. We also highlight the characteristics of Google’s existing synergetic data practices that will shape the development of not only Google Glass, but also all subsequent wearable mobile devices in light of 360-degree data collection. The key organizing (...) concept of our study is the disjuncture between institutional and policy forces in harnessing dual market mechanism, which frames how the new communication industry operates in the marketplace of ubiquitous personal advertising. We conclude by summarizing the three key areas of political-policy concern and suggest future solutions, with the discussion on the future of wearable computing practices related to the freedom of the human body. (shrink)

Public consumption of artificial intelligence (AI) technologies has been rarely investigated from the perspective of data surveillance and security. We show that the technology acceptance model, when properly modified with security and surveillance fears about AI, builds an insight on how individuals begin to use, accept, or evaluate AI and its automated decisions. We conducted two studies, and found positive roles of perceived ease of use (PEOU) and perceived usefulness (PU). AI security concern, however, negatively affected PEOU and PU, resulting (...) in less acceptance of AI—(1) use, (2) preference, and (3) participation. AI surveillance concern also had negative effects on the credibility of AI and its recommendations. We integrated extant literature on socio-demographic differences, providing an insight on how AI acceptance is based on one’s rationality regarding (1) technological risks (security/surveillance) and (2) benefits (PEOU/PU) as well as other contextual factors of socio-demographics. (shrink)