Generative AI System
AI systems trained on data that interact with individuals using text, audio, or visual communication, generating human-like output.
How the laws define this
Regulators agree that these systems center on technologies capable of creating realistic, human-like output across various formats, including text, audio, and visual media. Both instruments encompass tools that produce content mimicking human creation, whether described as outputs similar to those created by a human or as synthetic media. They diverge, however, in their focus and required obligations. Increase Transparency for Algorithmic Systems emphasizes data-trained systems that interact directly with individuals, explicitly conditioning its definition on generating human-like communication that mandates clear disclosure. In contrast, House Bill No. 10567 (Regulation of AI Use in 2025 Elections) defines the term around the production of realistic fake content and synthetic media, specifically calling out techniques like text-to-speech, voice cloning, face-swapping, and video or image synthesis using machine learning or other generative methods, without requiring direct user interaction or mentioning disclosure rules.
Synthesised from the 2 statutory definitions below. Each one is quoted in full, with its source.
Definitions (2)
AI systems trained on data that interact with individuals using text, audio, or visual communication, and are capable of generating output similar to that which could be created by a human, requiring clear disclosure.
Technologies and systems capable of producing synthetic media, including text-to-speech, voice cloning, face-swapping, video and image synthesis, and other AI techniques that generate realistic fake content, whether via machine learning models or other generative methods.