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AIAAIC uses a variety of methodologies and tools to examine and make the case for real AI and algorithmic transparency and openness.
Detection and classification of incidents and issues driven by and relating to AI, algorithms and automation.
Format: Dataset/database
Creation of a general purpose, open taxonomy of actual harms driven by AI, algorithmic and automation systems.
Format: Taxonomy, research paper
Examining AI and algorithmic harm and risk taxonomies from a human/user perspective.
Authors: Charlie Pownall, Maki Kanayama
Format: Working paper
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