Employees say they weren’t adequately warned about the brutality of some of the text and images they would be tasked with reviewing, and were offered no or inadequate psychological support. Workers were paid between $1.46 and $3.74 an hour, according to a Sama spokesperson.

  • AutoTL;DRB
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    281 year ago

    This is the best summary I could come up with:


    The 51 moderators in Nairobi working on Sama’s OpenAI account were tasked with reviewing texts, and some images, many depicting graphic scenes of violence, self-harm, murder, rape, necrophilia, child abuse, bestiality and incest, the petitioners say.

    “We are in agreement with those who call for fair and just employment, as it aligns with our mission – that providing meaningful, dignified, living wage work is the best way to permanently lift people out of poverty – and believe that we would already be compliant with any legislation or requirements that may be enacted in this space,” the Sama spokesperson said.

    In sample passages read by the Guardian, text that appeared to have been lifted from chat forums, include descriptions of suicide attempts, mass-shooting fantasies and racial slurs.

    The announcement coincided with an investigation by Time, detailing how nearly 200 young Africans in Sama’s Nairobi datacenter had been confronted with videos of murders, rapes, suicides and child sexual abuse as part of their work, earning as little as $1.50 an hour while doing so.

    She wants to see an investigation into the pay, mental health support and working conditions of all content moderation and data labeling offices in Kenya, plus greater protections for what she considers to be an “essential workforce”.


    I’m a bot and I’m open source!

    • @prd@beehaw.org
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      261 year ago

      A bot giving a summary of an article about people doing the work to train AI bots is some real snake-eating-its-own-tail shit.

      • JWBananas
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        11 year ago

        Thankfully this one doesn’t require AI. You can generally find the most important sentences in an article by counting the occurrences of every unique word, throwing out the common articles (e.g. a, an, the), and then extracting the sentences which contain the most frequently used words.