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

Assessments of algorithmic bias in large language models (LLMs) are generallycatered to uncovering systemic discrimination based on protectedcharacteristics such as sex and ethnicity. However, there are over 180documented cognitive biases that pervade human reasoning and decision makingthat are routinely ignored when discussing the ethical complexities of AI. Wedemonstrate the presence of these cognitive biases in LLMs and discuss theimplications of using biased reasoning under the guise of expertise. We callfor stronger education, risk management, and continued research as widespreadadoption of this technology increases. Finally, we close with a set of bestpractices for when and how to employ this technology as widespread adoptioncontinues to grow.

Challenging the appearance of machine intelligence: Cognitive bias in LLMs and Best Practices for Adoption