BACK TO ALL BLOGS Hive Joins in Endorsing the NO FAKES Act HiveApril 9, 2025 Contents The NO FAKES ActDetecting AI-Generated and Deepfake Content Today, Hive joins other leading technology companies and trade organizations in endorsing the NO FAKES Act — a bipartisan piece of legislation aimed at addressing the misuse of generative AI technologies by bad actors. The legislation has been introduced by U.S. Senators Marsha Blackburn (R-Tenn.), Chris Coons (D-Del.), Thom Tillis (R-N.C.), and Amy Klobuchar (D-Minn.), along with U.S. Representatives Maria Salazar (R-Fla.) and Madeleine Dean (D-Penn.). Read the full letter here. The NO FAKES Act The Nurture Originals, Foster Art, and Keep Entertainment Safe (NO FAKES) Act of 2025 is a bipartisan bill that would protect the voice and visual likeness of all individuals from unauthorized recreations by generative artificial intelligence. This Act, aimed at addressing the use of non-consensual digital replications for audiovisual works or sound recordings, will hold individuals or companies liable for the production of such content and hold platforms liable for knowingly hosting such content. As a leading provider of AI solutions to hundreds of the world’s largest and most innovative organizations, Hive understands firsthand the extraordinary benefits that generative AI technology provides. However, we also recognize that bad actors are relentless in their attempts to exploit it. As Kevin Guo, Hive’s CEO and Cofounder, explains in the endorsement letter: “The development of AI-generated media and AI detection technologies must evolve in parallel,” said Kevin Guo, CEO and cofounder of Hive. “We envision a future where AI-generated media is created with permission, clearly identified, and appropriately credited. We stand firmly behind the NO FAKES Act as a fundamental step in establishing oversight while keeping pace with advancements in artificial intelligence to protect public trust and creative industries alike.”https://www.blackburn.senate.gov/2025/4/technology/blackburn-coons-salazar-dean-colleagues-introduce-no-fakes-act-to-protect-individuals-and-creators-from-digital-replicas To this end, Hive has commercialized AI-powered solutions to help digital platforms proactively detect the potential misuse of AI-generated and synthetic content. Detecting AI-Generated and Deepfake Content Hive’s AI-generated and deepfake detection models can help technology companies identify unauthorized digital replications of audiovisual likeness in order to comply with the provisions outlined in the NO FAKES Act. The endorsement letter references the high-profile example of the song “Heart on My Sleeve,” featuring unauthorized AI-generated replicas of the voices of Drake and The Weeknd, which was played hundreds of thousands of times before being identified as fake. Streaming platforms and record labels will be able to leverage Hive’s AI-Generated Music model to proactively detect such instances of unauthorized recreations and swiftly remove them. While the harmful effects of unauthorized AI-generated content go far beyond celebrities, Hive also offers a Celebrity Recognition API, which detects the visual likeness of a broad index of well known public figures, from celebrities and influencers to politicians and athletes. Hive’s Celebrity Recognition API can help platforms proactively identify bad actors misusing celebrity visual likeness to disseminate false information or unauthorized advertisements, such as the recent unauthorized synthetic replica of Tom Hanks promoting a dental plan. Hive’s AI-generated and deepfake detection solutions are already trusted by the United States Department of Defense to combat sophisticated disinformation campaigns and synthetic media threats. For more information on Hive’s AI-Generated and Deepfake Detection solutions, reach out to sales@thehive.ai or visit: https://thehive.ai/apis/ai-generated-content-classification
BACK TO ALL BLOGS Model Explainability With Text Moderation HiveDecember 2, 2024February 21, 2025 Contents The Need For ExplainabilityA Supplement to Our Text Moderation ModelComprehensive Language Support Hive is excited to announce that we are releasing a new API: Text Moderation Explanations! This API helps customers understand why our Text Moderation model assigns text strings particular scores. The Need For Explainability Hive’s Text Moderation API scans a text-string or message, interprets it, and returns to our users a score from 0-3 mapping to a severity level across a number of top level classes and dozens of languages. Today, hundreds of customers send billions of text strings each month through this API to protect their online communities. A top feature request has been explanations for why our model assigns the scores it does, especially for foreign languages. While some moderation scores may be clear, there also may be ambiguity around edge cases for why a string was scored the way it was. This is where our new Text Moderation Explanations API comes in—delivering additional context and visibility into moderation results in a scalable way. With Text Moderation Explanations, human moderators can quickly interpret results and utilize the additional information to take appropriate action. A Supplement to Our Text Moderation Model Our Text Moderation classes are ordered by severity, ranging from level 3 (most severe) to level 0 (benign). These classes correspond to the possible scores Text Moderation can give a text string. For example: If a text string falls under the “sexual” head and contains sexually explicit language, it would be given a score of 3. The Text Moderation Explanations API takes in three inputs: a text string, its class label (either “sexual”, “bullying”, “hate”, or “violence”), and the score it was assigned (either 3, 2, 1, or 0). The output is a text string that explains why the original input text was given that score relative to its class. It should be noted that Explanations is only supported for select multilevel heads (corresponding to the class labels listed previously). To develop the Explanations model, we used a supervised fine-tuning process. We used labeled data—which we internally labeled at Hive using native speakers—to fine-tune the original model for this specialized process. This process allows us to support a number of languages apart from English. Comprehensive Language Support We have built our Text Moderation Explanation API with broad initial language support. Language support solves the crucial issue of understanding why a text string (in one’s non-native language) was scored a certain way. We currently support eight different languages for Text Moderation Explanations and four top level classes: Text Moderation Explanations are now included at no additional cost as part of our Moderation Dashboard product, as shown below: Additionally, customers can also access the Text Moderation Explanations model through an API (refer to the documentation). In future releases, we anticipate adding further language and top level class support. If you’re interested in learning more or gaining test access to the Text Moderation Explanations model, please reach out to our sales team (sales@thehive.ai) or contact us here for further questions.