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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The adoption of generative AI technologies is swiftly expanding. Servicesemploying both linguistic and mul-timodal models are evolving, offering usersincreasingly precise responses. Consequently, human reliance on thesetechnologies is expected to grow rapidly. With the premise that people will beimpacted by the output of AI, we explored approaches to help AI output producebetter results. Initially, we evaluated how contemporary AI servicescompetitively meet user needs, then examined society's depiction as mirrored byLarge Language Models (LLMs). We did a query experiment, querying about socialconventions in various countries and eliciting a one-word response. We comparedthe LLMs' value judgments with public data and suggested an model ofdecision-making in value-conflicting scenarios which could be adopted forfuture machine value judgments. This paper advocates for a practical approachto using AI as a tool for investigating other remote worlds. This re-search hassignificance in implicitly rejecting the notion of AI making value judgmentsand instead arguing a more critical perspective on the environment that defersjudgmental capabilities to individuals. We anticipate this study will empoweranyone, regardless of their capacity, to receive safe and accurate valuejudgment-based out-puts effectively.

Evaluating and Improving Value Judgments in AI: A Scenario-Based Study on Large Language Models' Depiction of Social Conventions