OpenAI claims that it’s developed a way to use GPT-4, its flagship generative AI model, for content moderation — lightening the burden on human teams.
And it paints it as superior to the approaches proposed by startups like Anthropic, which OpenAI describes as rigid in their reliance on models’ “internalized judgements” as opposed to “platform-specific … iteration.”
Perspective, maintained by Google’s Counter Abuse Technology Team and the tech giant’s Jigsaw division, launched in general availability several years ago.
Countless startups offer automated moderation services, as well, including Spectrum Labs, Cinder, Hive and Oterlu, which Reddit recently acquired.
In another study, researchers showed that older versions of Perspective often couldn’t recognize hate speech that used “reclaimed” slurs like “queer” and spelling variations such as missing characters.
Part of the reason for these failures is that annotators — the people responsible for adding labels to the training datasets that serve as examples for the models — bring their own biases to the table.
This is the best summary I could come up with:
OpenAI claims that it’s developed a way to use GPT-4, its flagship generative AI model, for content moderation — lightening the burden on human teams.
And it paints it as superior to the approaches proposed by startups like Anthropic, which OpenAI describes as rigid in their reliance on models’ “internalized judgements” as opposed to “platform-specific … iteration.”
Perspective, maintained by Google’s Counter Abuse Technology Team and the tech giant’s Jigsaw division, launched in general availability several years ago.
Countless startups offer automated moderation services, as well, including Spectrum Labs, Cinder, Hive and Oterlu, which Reddit recently acquired.
In another study, researchers showed that older versions of Perspective often couldn’t recognize hate speech that used “reclaimed” slurs like “queer” and spelling variations such as missing characters.
Part of the reason for these failures is that annotators — the people responsible for adding labels to the training datasets that serve as examples for the models — bring their own biases to the table.
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