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Google Cloud Textual content Moderation | Google Cloud Weblog


To empower builders to establish delicate content material in a quickly altering media atmosphere, we’re excited to announce Textual content Moderation powered by PaLM 2, obtainable by way of the Cloud Pure Language API. Inbuilt collaboration with Jigsaw and Google Analysis, Textual content Moderation helps organizations scan for delicate or dangerous content material. Listed here are some examples of how the Textual content Moderation service can be utilized:

  • Model Security: Shield in opposition to user-generated content material and writer content material which are thought of not “model secure” for the advertiser  

  • Consumer safety: Scan for doubtlessly offensive or dangerous content material

  • Generative AI threat mitigation: Assist safeguard in opposition to the era of inappropriate content material in outputs from generative fashions

Promote model security

Model security is a set of procedures that purpose to guard the status and trustworthiness of a model within the digital age. One of many greatest dangers to model security is the content material that adverts are related to; if an advert seems on a web site that incorporates content material that doesn’t conform with the sponsoring model’s values, it may possibly mirror poorly on the model and group, so it’s essential for corporations to establish and take away content material that isn’t aligned with model tips or in line with the model. 

Textual content Moderation can be utilized by our prospects to establish content material that they decide is offensive or dangerous, delicate in context, or in any other case inappropriate for his or her model. As soon as a corporation has recognized this content material, groups can take steps to take away it from promoting campaigns or stop it from being related to the model sooner or later, serving to be sure that promoting campaigns are efficient and that the model is related to optimistic and reliable content material.

Shield customers from dangerous content material

Digital media platforms, gaming publishers, and on-line marketplaces all have a vested curiosity in mitigating the dangers of user-generated content material. They need to present a secure and welcoming atmosphere for his or her customers whereas additionally sustaining an open and free alternate of concepts. Textual content Moderation might help them obtain this objective, utilizing synthetic neural networks to detect and take away dangerous content material, equivalent to harassment or abuse. These efforts might help scale back hurt, enhance buyer expertise, and enhance buyer retention.

Mitigate dangers of generative fashions

Over the past yr, progress in AI has enabled software program to extra reliably generate textual content, photos, and video, resulting in new services that use machine studying, together with textual content mills, to create content material. Nonetheless, with any AI content material era, there’s a threat of manufacturing offensive materials, even inadvertently. 

To handle this threat, we’ve educated and evaluated the Textual content Moderation service on actual prompts and responses from giant generative fashions. Textual content Moderation is flexible and covers a broad vary of content material varieties, making it a robust software for safeguarding customers from dangerous content material.

Getting began with Textual content Moderation utilizing the Pure Language API

Textual content Moderation is powered by Google’s newest PaLM 2 basis mannequin to establish a variety of dangerous content material, together with hate speech, bullying, and sexual harassment. Simple to make use of and combine with current programs, the API could be accessed from nearly any programming language to return confidence scores throughout 16 completely different “security attributes.”

Go to the Pure Language AI web site to present it a attempt to consult with the “Textual content Moderation” web page for particulars. You may additionally check out the Textual content Moderation codelab right here.


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