Abstract
INTRODUCTION: The increased ability of Artificial Intelligence (AI) technologies to generate and parse texts will inevitably lead to more proposals for AI’s use in the semantic sentiment analysis (SSA) of textual sources. We argue that instead of focusing solely on debating the merits of automated v
Highlights
The increased ability of Artificial Intelligence (AI) technologies to generate and parse texts will inevitably lead to more proposals for AI’s use in the semantic sentiment analysis (SSA) of textual sources
Where the many views of moral philosophers diverge when it comes to trust, is in their assumptions regarding what values, behaviors, systems, and paradigms result in a desirable form of “good.” They may differ in their perspectives regarding what mechanisms are most essential, but we posit that using a system like MultiVerse is an example of enabling infrastructure that does not force a choice of perspectives, but instead, supports those that can be aided by greater tracking of contributions and intentions of different individuals involved in the translation and transmission of information
To demonstrate how rethinking underlying technical infrastructure can reshape the questions we face with AI, we illustrated an example of one such “rethought” realization of a data storage system
Summary
Artificial Intelligence (AI) is increasingly touching and structuring our lives. AI helps enhance our ordinary lives with tailored news, better traffic predictions,. The need to automatically identify bad actors posting online news [11, 12] or social media [13, 14], can wrongfully limit an individual’s freedom of speech or be gamed effectively by deliberate bad actors or states. These situations contextualize the ethical and professional domain of the hypothetical cyber-archivist, the AI librarian or scholarly assistant who processes written data and annotates it for further analysis or classification.
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