The Least Talked About AI Regulations and Their Significance

While global AI regulation discussions frequently spotlight high-profile initiatives like the European Union’s Artificial Intelligence Act (AI Act) or the proposed U.S. federal AI framework, several lesser-known regulations are decisively shaping artificial intelligence's ethical and legal landscape. These often-overlooked policies address critical gaps in AI governance, aiming to protect individuals and ensure responsible AI deployment. Here, we delve into some of the least discussed AI regulations and their significant implications.

A chained robot symbolizes the crucial role of AI regulations in shaping the future of artificial intelligence.

A robot trapped in chains symbolizes the complex web of AI regulations and their often underestimated but significant effects.

1. The No FAKES Act (United States)

One of the most distinctive regulatory efforts is the proposed No FAKES Act in the United States. This draft legislation targets generative AI systems' unauthorized use of individuals' voices and visual likenesses. In an era where deepfakes and AI-generated content are increasingly sophisticated, this regulation seeks to prevent the exploitation of personal identity for fraudulent or malicious purposes. The No FAKES Act could become a landmark law protecting individuals from AI-driven impersonations (“AI Watch,” White & Case).

2. Utah’s AI Policy Act

Utah’s AI Policy Act exemplifies a state-level approach to AI governance, requiring transparency in using AI systems during interactions. The regulation mandates entities to disclose when AI tools are being used, ensuring individuals are informed about the presence of automated systems. This emphasis on transparency aligns with ethical AI principles and could set a precedent for similar state-level policies across the United States (Reuters, 2024).

3. Employment AI Regulations in Illinois and New York City

Illinois and New York City have taken decisive steps to regulate the use of AI in employment decisions. These laws require employers to conduct bias audits on AI-driven hiring tools and ensure compliance with anti-discrimination laws. By addressing the potential for AI to perpetuate bias, these regulations strive to create a fairer employment landscape and uphold principles of equity (“Comparing EU and U.S. AI Legislation,” Reuters).

4. California’s Legislation on AI in Mental Health Services

A particularly specialized yet impactful regulatory effort is California’s HB1974, introduced in early 2023. This bill aims to regulate the use of AI in mental health services by requiring:

• Pre-approval from licensing boards before AI systems are used in treatment.

• Continuous professional oversight of AI tools to ensure safety and efficacy.

• Full disclosure to patients about the involvement of AI in their care.

• Informed consent from patients before integrating AI systems into their treatment plans.

This regulation addresses concerns over the ethical implications of using AI in sensitive healthcare settings, ensuring that human oversight remains central (“U.S. State-by-State Artificial Intelligence Legislation Snapshot,” Bryan Cave Leighton Paisner LLP).

Why These Regulations Matter

Though less publicized, these regulations fill critical gaps in the broader AI governance landscape. They tackle AI technologies' specific challenges, such as identity theft, bias, and ethical concerns in sensitive industries. Moreover, these laws underscore transparency, fairness, and accountability—principles vital for building public trust in AI.

Conclusion

The AI regulations that often go unnoticed confidently highlight the nuanced and localized strategies for tackling AI challenges. As AI usage expands, these policies are poised to become essential models for broader regulatory frameworks. By examining these lesser-known initiatives, policymakers and stakeholders can better understand how to govern AI technologies effectively in diverse contexts.examining these lesser-known initiatives, policymakers and stakeholders can gain a deeper understanding of how to effectively govern AI technologies in diverse contexts.