LEarn

This is a resource for legislators, policy makers, journalists, thought leaders, and researchers.

Artificial intelligence can be confusing and overwhelming. We aim to provide clarity and understanding.

The modules, articles, and guides presented here are intended to explain fundamental concepts in artificial intelligence and AI governance in accurate and non-technical language. New articles will be added as the technology and language of AI evolve—and they’re evolving quickly.

topics

AI 101

Companion Chatbots

TCAI Research: Further Resources

The AI Developer's Duty of Care

AI Safeguards

Training Data Transparency

Disclosing AI Use

AI 101

Your startup guide to understanding Artificial Intelligence.

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Defining Artificial Intelligence

While common algorithms follow step-by-step instructions to solve specific tasks, AI systems can analyze data, recognize patterns, and improve their performance over time.

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How AI Systems Are Created

An AI model requires enormous amounts of computing power and massive datasets. By ingesting the datasets, the model “learns” about the structure of language or patterns derived from millions of images.

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Why the AI Boom Is Happening Now

AI systems like ChatGPT and Gemini require two things: enormous computing power and massive datasets. Those assets have only become available in recent years, fueling the boom.

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companion chatbots

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Companion Chatbots 101

Companion chatbots are digital characters created by powerful AI systems, designed to respond to a consumer in a conversational, lifelike fashion.

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Growing Niche: Romantic Companion Bots

One of the fastest growing sectors within the companion chatbot industry is the romantic companion chatbot. These are also called intimate chatbots.

A recent assessment of companion chatbots, including products offered by CharacterAI and Replika, concluded that the products present a real risk of harm to children and teenagers.

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What they are, how they work, where the risks and dangers lie.

AI Safeguards

Exploring the foundations of AI safeguards and mitigation.

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AI Safeguards: Where to Start

At the Transparency Coalition we believe AI policy discussion and legislative action happen at many levels simultaneously. Our mission is to address known AI safety and privacy risks with practical solutions. We’re focused on bringing transparency to both AI inputs and AI outputs.

Transparency in AI training data is the foundation of ethical AI.

State legislatures should consider measures that require developers of AI systems and services to publicly disclose specified information related to the datasets used to train their products.

At the Transparency Coalition we believe the most important AI output provision is also the most basic: Disclose the use of AI.

California’s AI Transparency Act, adopted in Sept. 2024, provides a model for this kind of disclosure. The Act requires AI developers to embed detection tools in the media their AI creates, and to post an AI decoder tool on their website.

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The ai developer’s Duty of care

Learn about duty of care, product liability, and how these concepts apply to artificial intelligence products.

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What is a ‘duty of care’ and how does it apply to artificial intelligence?

AI developers and deployers have a duty of care that’s no different than any other product manufacturer. Here’s what that means.

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Mar 12, 2025

Legislators have filed AI ‘duty of care’ liability bills in 10 states. Here’s how to track them.

Mar 12, 2025

11 AI liability bills have been filed in 10 states, as of mid-March. We’ve built a tracker to follow all of them.

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Mar 12, 2025

Jan 6, 2025

Transparency Coalition makes AI ‘duty of care’ laws a top 2025 legislative priority

Jan 6, 2025

Here’s why TCAI advocates for the adoption of strong, sensible product liability laws that encompass artificial intelligence systems.

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Jan 6, 2025

Training Data transparency

Learn about the foundational ingredients of AI models, and why and how they should be disclosed.

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Training Data: What the Machine Learns

Training data is the foundation of artificial intelligence. It’s what AI systems like ChatGPT use to provide answers to the prompts we provide. It’s what generative image systems like Midjourney and DALL-E use to conjure AI-created art.

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Why Training Data Is Not a Trade Secret

Training data is the foundation of artificial intelligence. It’s what AI systems like ChatGPT use to provide answers to the prompts we provide. It’s what generative image systems like Midjourney and DALL-E use to conjure AI-created art.

Developers of AI systems should be required to provide documentation for all training data used in the development of an AI model.

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DISCLOSING AI USE

Understand the importance of AI disclosure laws, and how content provenance makes disclosure possible.

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Why and How to Disclose the Use of AI

With the emergence of generative AI, it now takes just a few button-clicks for anyone to create or manipulate data and convince others that fake content is real.

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Emerging Standards in Disclosure

Today most media/tech companies are coalescing around the standard created by the Coalition for Content Provenance and Authenticity (C2PA) .

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Legislating the Disclosure of AI Use

Legislative policies requiring the disclosure of the use of AI are developing side-by-side along the emerging standards in AI provenance. They’re not perfectly in sync—and that’s okay.

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TCAI research: further resources

TCAI guides to AI lawsuits, state data privacy laws, and more.

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TCAI Guide: How to stop your images and data from being used to train AI

Amazon’s decision to not allow consumers to opt out of Alexa’s collection of consumer data (including a person’s voice) for AI training has sparked renewed interest in blocking the AI use of data from other platforms and products.

Here’s how to opt out of AI training in Alexa, Pinterest, LinkedIn, Microsoft 365, and other products.

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TCAI Guide to Search Tools: Was Your Data Used to Train an AI Model?

Search engines have emerged recently that allow individuals to check specific types of content—books and images—for use as AI training data.

We link to the search tools, and include tips on preventing your data from being used to train AI models.

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TCAI Guide to AI Lawsuits

The hailstorm of AI-related lawsuits over the past year can make the litigation space feel chaotic and confusing. In fact, the lawsuits can be roughly sorted into two buckets: copyright infringement and harmful AI-driven outcomes.

This TCAI curated guide offers a clear and concise overview of today’s AI legal battlefield.

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Complete Resource Library

Bruce Barcott 10/31/24 Bruce Barcott 10/31/24

Understanding Synthetic Data

In today’s AI ecosystem there are two general types of training data: organic and synthetic.

Organic data describes information generated by actual humans, whether that’s a piece of writing, a numerical dataset, a song, an image, or a video. Synthetic data is created by generative AI models using organic data as a base material.

Synthetic Data and AI ‘Model Collapse’

Just as a photocopy of a photocopy can drift away from the original, when generative AI is trained on its own synthetic data, its output can also drift away from reality, growing further apart from the organic data that it was intended to imitate.

Transparency and Synthetic Data

The use of synthetic data isn’t inherently good or bad. In medical research, for example, it’s a critically important tool that allows scientists to make new discoveries while protecting the privacy of individual patients.

At the Transparency Coalition, we are not calling for limitations on the creation or use of synthetic data. What’s needed is disclosure: Developers should be transparent in their use of synthetic data when using it to train an AI model.