Navigating AI Disclosure Requirements

While the race to integrate AI into business operations is accelerating, the regulatory landscape remains fragmented. Companies are navigating a complex web of laws and guidelines that vary widely by jurisdiction and context. Currently, there is no comprehensive federal law in the United States mandating AI disclosure. This regulatory gap leaves businesses to grapple with a patchwork of existing laws and targeted mandates, such as Illinois' AI Video Interview Act and California's AB 2013.

Regulators are increasingly asserting that existing legal authorities apply to AI, much like they do to other innovative technologies. For instance, the Federal Trade Commission (FTC) stated that "existing legal authorities apply to the use of automated systems and innovative new technologies just as they apply to other practices" [https://natlawreview.com/article/three-trends-ai-regulation-2023-ai-washington-report].

In specific contexts, companies must comply with targeted disclosure requirements. For example, Illinois' Artificial Intelligence Video Interview Act requires employers to notify applicants when AI is used to analyze video interviews, explain how the AI operates, obtain consent, and destroy the video recordings within 30 days if requested by the applicant [https://www.hsfkramer.com/insights/2023-12/ai-regulatory-update]. Similarly, California's Generative AI Training Data Transparency Act (AB 2013) imposes training data disclosure obligations that are currently in effect [https://www.corporatecomplianceinsights.com/top-stories-package-2023/].

The EU AI Act sets a precedent for risk-based AI regulation. The Act categorizes AI systems into four risk tiers and imposes stricter rules as the risk level increases. Non-compliance can result in fines ranging from €7.5 million or 1.5% of global revenue to €35 million or 7% of global revenue [https://www.hsfkramer.com/insights/2023-12/ai-regulatory-update]. This risk-based approach is expected to influence global standards for AI regulation.

In practice, most AI transparency remains voluntary and is declining. A recent study by Stanford's Institute for Human-Centered Artificial Intelligence (HAI) found that industry transparency averaged just 40/100 in 2025, down from 58/100 in 2024 [https://hai.stanford.edu/news/transparency-in-ai-is-on-the-decline]. This decline highlights the need for companies like Worqd to prioritize transparency in their AI practices, ensuring they clearly communicate how their AI systems operate. This transparency not only builds trust with clients but also aligns with best practices recommended by industry experts.

Companies must also be aware of the enforcement risks associated with misleading AI claims. According to SEC Chair Gary Gensler, regulators are scrutinizing AI disclosures for accuracy and will penalize companies that engage in "AI-washing" [https://www.hsfkramer.com/insights/2023-12/ai-regulatory-update]. This stance underscores the importance of honest and specific AI claims, a principle that Worqd adheres to in its anti-fabrication policy.

Here are some key points to consider when navigating AI disclosure requirements:

• Understand the specific disclosure mandates in your jurisdiction and industry. For example, companies using AI in hiring processes must comply with Illinois' AI Video Interview Act.
• Be transparent about AI use in customer-facing operations. Companies should clearly explain how AI is used in their services to build trust and comply with evolving regulations.
• Avoid misleading AI claims. Misrepresenting AI capabilities can lead to regulatory penalties and damage your company's reputation.
• Prepare for international regulations. If your business operates in the EU, ensure compliance with the EU AI Act, which imposes risk-based requirements and significant fines for non-compliance.
• Check provider compliance practices. When evaluating an AI growth partner, Worqd included, ask how AI is used in outreach and data handling to ensure compliance and transparency.

Companies should stay informed about the evolving regulatory landscape and proactively implement transparency measures. By doing so, they can build trust with customers and mitigate regulatory risks. Worqd, for example, emphasizes clear communication about AI usage in its lead generation and demand generation services, aligning with best practices and regulatory expectations.

The Importance of Transparency in AI Use

Here's the uncomfortable truth about AI transparency right now: the companies building the most powerful AI systems are becoming less open about them, not more. A Stanford research team found that transparency has declined since 2024, with companies diverging greatly in how open they are about their AI practices.

The numbers tell the story. In the 2025 Foundation Model Transparency Index, 13 major AI companies averaged just 40 out of 100 points — down from 58 the year before. Meta fell from 60 to 31, and OpenAI went from first place in 2023 to second-to-last in 2025. The researchers concluded that transparency depends mostly on whether individual companies choose to prioritize it, not on industry-wide pressure.

That voluntary decline is exactly why regulators have stepped in. SEC Chair Gary Gensler has warned companies against "AI-washing" — overstating AI use or capabilities to sound more innovative. He put it plainly: businesses must provide clear and concise disclosure about their use of AI, and regulators will scrutinize those disclosures for accuracy. FTC Chair Lina Khan has taken a similar stance, arguing the FTC is well equipped to handle issues raised by the rapidly developing AI sector under existing legal authorities.

So while no universal law forces companies to announce every use of AI, regulators punish something more specific: inaccurate or misleading claims about AI. That changes how you should evaluate any provider using AI on your behalf. When you're checking a company's compliance practices, look for signs they treat disclosure as a duty, not an afterthought:

• Do they describe their AI systems in plain language, or hide behind vague marketing terms?
• Can they explain exactly how AI touches your data, your outreach, and your customer interactions?
• Do their claims come with evidence, or do they rely on inflated numbers and guarantees?
• Do they get explicit consent before automated systems contact people on your behalf?

This matters because privacy laws already require organizations to be transparent about what data they collect and how they use it — and compliance has shifted from "good to have" to a basic requirement for doing business. When we built Worqd's processes, this shaped real decisions: our booking funnel requires explicit consent before contact, and our anti-fabrication policy means we don't publish results until real evidence backs them up. Clear, accurate disclosure isn't just compliance — with industry transparency averaging 40/100, it's how an AI provider earns trust that most won't.

Implementing AI Disclosure Best Practices

Most companies will never face an AI disclosure fine — but the ones that get in trouble almost always made misleading claims. That's the practical takeaway from the current regulatory landscape: enforcement targets inaccurate AI statements, not AI use itself. SEC Chair Gary Gensler coined the term "AI-washing" and warned regulators will scrutinize AI disclosures to ensure they are "accurate and not misleading" (according to legal analysis).

Start by checking any provider's compliance practices before you sign. Privacy laws already require organizations to be transparent about what data they collect and how they use it, and compliance has shifted from "good to have" to a basic requirement for conducting business (compliance research shows). Ask how AI is used in outreach, where data goes, and what consent is collected.

Next, map your obligations to the EU AI Act's risk-based framework if you have any international reach. The rule is simple: "the higher the risk, the stricter the rules" — and noncompliance can trigger fines up to €35 million or 7% of global revenue (legal experts note). The framework applies to companies whose AI output is used in the EU, so even US-based businesses serving international customers fall within scope. The EU approach is also expected to set a global standard for other jurisdictions.

Finally, treat voluntary transparency as a competitive edge rather than a burden. Stanford HAI's 2025 Foundation Model Transparency Index found industry transparency averaged just 40/100, down from 58/100 in 2024 (the research team reported). In a market that opaque, a company that plainly explains its AI use stands out. Partnership on AI's guidance makes the same point: transparency and accountability mechanisms must accompany AI implementation (its newsroom principles state).

A few practices make disclosure straightforward:

• Use plain language — describe "our AI systems" and "fast follow-up," not jargon that obscures what's happening.
• Collect explicit consent — Worqd's booking funnel, for example, requires an "I agree to be contacted" checkbox and states exactly how details will be used.
• Keep outreach permission-aware — personalized contact to relevant accounts, the opposite of a template blast.
• Never overstate results — honest, specific claims about AI performance are both compliant and more credible than inflated ones.

The pattern across all of this: the companies handling AI disclosure well aren't doing it because a single law forces them to. They're doing it because clear, honest communication about AI builds trust — and because regulators punish the opposite.