What is the EU's AI Code of Practice?

The EU's AI Code of Practice is a voluntary framework that establishes standards for transparency, accountability, and ethical conduct in artificial intelligence development and deployment. Companies that sign on commit to providing clear information about how their AI systems work, ensuring human oversight, and implementing safeguards against potential harms. While voluntary, this code is viewed as a precursor to the EU's more comprehensive AI Act, which will create legally binding requirements.

What is RegTech and why does it matter for AI?

RegTech (Regulatory Technology) refers to companies that provide technology solutions to help businesses comply with regulations efficiently and effectively. For AI systems, RegTech solutions include tools for transparency reporting, bias detection, data lineage tracking, and audit capabilities. As AI regulations increase globally, RegTech becomes essential because it allows companies to innovate while still meeting complex compliance requirements, potentially saving them from costly penalties and reputation damage.

Why are Microsoft and Meta taking different approaches to AI regulation?

Microsoft and Meta represent two competing strategic approaches to AI development. Microsoft has embraced a "compliance-first" strategy, viewing regulatory adherence as a competitive advantage and way to build trust. Meta has chosen an "innovation-first" approach, concerned that early adoption of restrictive standards could hamper development and give advantages to less regulated competitors. This split reflects a fundamental industry debate about whether regulation stifles innovation or creates more sustainable growth opportunities through increased trust and standardization.

How might investing in AI compliance companies be different from general AI investments?

While general AI investments often focus on companies developing cutting-edge algorithms or applications, AI compliance investments target the infrastructure needed to make AI deployments trustworthy and legally compliant. These compliance-focused companies may offer more stability during regulatory crackdowns, as their services become more essential when rules tighten. They might also have more predictable revenue streams as compliance requirements are mandatory, unlike optional AI features. However, they may experience less dramatic growth than breakthrough AI application companies.

What are data lineage and algorithmic accountability?

Data lineage refers to the complete history of data throughout its lifecycle - where it originated, how it's been processed, who has accessed it, and how it's been used. For AI systems, maintaining data lineage is crucial for explaining why models make certain decisions. Algorithmic accountability involves making AI systems transparent and explainable, ensuring there are mechanisms to review decisions, identify biases, and assign responsibility when problems occur. Both concepts are central to regulatory requirements, as they enable oversight of AI systems that might otherwise operate as "black boxes."

15 handpicked stocks

AI Compliance Divergence: RegTech Solutions for EU Standards

This carefully selected group of stocks represents companies at the forefront of AI governance and compliance solutions. Our professional analysts have identified these opportunities emerging from the regulatory divide between tech giants like Microsoft and Meta, as businesses increasingly need specialized help to navigate complex AI rules.

Han Tan | Market Analyst

Updated 2 days ago | Published at जुलाई 20