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Screecap: "These two recommendations will need to be implemented with care. We have already noted the potential barrier to access. Secrecy concerns may also arise in some situations (e.g., some groups may be willing to share datasets but not demographic information, for fear of public relations backlash or to protect the safety of contributors to the dataset). That said, as consumers of datasets or products trained with them, NLP researchers, developers, and the general public would be well advised to use systems only if there is access to the information we propose should be included in data statements." Last sentence highlighted in blue. Source: https://aclanthology.org/Q18-1041.pdf

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Screecap: "These two recommendations will need to be implemented with care. We have already noted the potential barrier to access. Secrecy concerns may also arise in some situations (e.g., some groups may be willing to share datasets but not demographic information, for fear of public relations backlash or to protect the safety of contributors to the dataset). That said, as consumers of datasets or products trained with them, NLP researchers, developers, and the general public would be well advised to use systems only if there is access to the information we propose should be included in data statements." Last sentence highlighted in blue. Source: https://aclanthology.org/Q18-1041.pdf
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Prof. Emily M. Bender(she/her) (emilymbender@dair-community.social)'s status on Friday, 08-Dec-2023 20:30:25 JST Prof. Emily M. Bender(she/her)

A quick thread on #AIhype and other issues in yesterday's Gemini release:
#1 -- What an utter lack of transparency. Researchers form multiple groups, including @meg and @timnitGebru when they were at Google, have been calling for clear and thorough documentation of training data & trained models since 2017.
In Bender & Friedman 2018, we put it like this:
/1

In conversation about a year ago from dair-community.social permalink