This research attempts to construct a second order observation model in understanding the significance of Artificial intelligence (AI) in changing the global power structure. Because of the inevitable ubiquity of AI in the world societies’ near future, it impacts all the sections of society triggering socio-technical iterative developments. Its horizontal impact and states’ race to become leader in the AI world asks for a vivid understanding of its impact on the international system. To understand the latter, Triple Helix (TH) model (...) along with Shannon’s information entropy has been used to operationalize system’s theory. This model uses Shannon’s information theory to calculate the uncertainty generated from the interactions between the sub-systems considered. Data for the latter has been taken from Sanford Artificial Intelligence Laboratory 2019 report. It is found that European countries are the most effected in the AI era, with probability of losing their global influence and thus creating power void. Emerging powers such as India, Canada, South Africa and Brazil have better chances to fill the void and emerge as global influences. (shrink)

Programming artificial intelligence to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair or fair. It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be willing (...) to accept if done to themselves. Secondly, that such a construal is ontologically reflected in Word Embeddings, by virtue of their ability to reflect the dimensions of such a perception. These dimensions being: responsibility vs. irresponsibility, gain vs. loss, reward vs. sanction, joy vs. pain, all as a single vector. The paper finds it possible to quantify and qualify a verb as fair or unfair by calculating the cosine similarity of the said verb’s embedding vector against FairVec—which represents the above dimensions. We apply this to Glove and Word2Vec embeddings. Testing on a list of verbs produces an F1 score of 95.7, which is improved to 97.0. Lastly, a demonstration of the method’s applicability to sentence measurement is carried out. (shrink)

AI technologies hold great promise for addressing existing problems in organizational contexts, but the potential benefits must not obscure the potential perils associated with AI. In this article, we conceptually explore these promises and perils by examining AI use in organizational contexts. The exploration complements and extends extant literature on AI management by providing a typology describing four types of AI use, based on the idea of co-constitution of AI technologies and organizational context. Building on this typology, we propose three (...) recommendations for informed use of AI in contemporary organizations. First, explicitly define the purpose of organizational AI use. Second, define the appropriate level of transparency and algorithmic management for organizational AI use. Third, be aware of AI’s context-dependent nature. (shrink)