CQ | Why AI Companies Should Not Be Allowed to Self-Regulate: Lessons from High-Risk Industries

As artificial intelligence redefines entire industries, companies in the field are increasingly demanding the freedom to self-regulate. But history shows us that in high-risk sectors, this approach can have serious consequences.

What can we learn from the way aviation or the banking sector manage risks, and why does AI need real external oversight? The answers could mean the difference between responsible progress and social crisis.

Context: Why Is Self-Regulation in AI Being Debated Now?

The rapid development of artificial intelligence (AI) has put enormous pressure on technology companies to innovate and bring increasingly sophisticated products to market. At the same time, more and more questions are being raised about the social, ethical, and economic impact of these technologies. In the absence of clear regulations, many AI companies claim they can implement their own standards of ethics and safety, promoting self-regulation as the optimal solution to avoid stifling innovation.

However, this approach raises fundamental questions: can companies truly monitor themselves when the stakes are so high? What guarantees are there that commercial interests will not prevail over public safety?

Examples from High-Risk Industries: Aviation and the Banking Sector

Recent history shows that in industries with major potential impact on society, self-regulation has never been sufficient. Two relevant examples are aviation and the banking sector:

• Aviation: This is one of the most regulated industries in the world. Organizations such as EASA (European Union Aviation Safety Agency) and FAA (Federal Aviation Administration) impose strict safety standards, regular audits, and independent investigations for every incident. Without these measures, accidents and errors would be much more frequent.
• Banking sector: After the 2008 financial crisis, regulations became significantly stricter. Banks are subject to external controls, audits, and transparency requirements precisely to prevent systemic risks and abuses that can affect millions of people.

The Risks of Self-Regulation in AI

AI has the potential to influence critical decisions in healthcare, justice, finance, or infrastructure. Allowing companies to set their own rules involves major risks:

• Lack of transparency: Without external obligations, companies can hide or downplay incidents, errors, or abuses.
• Conflicts of interest: Commercial interests can conflict with ethical or safety concerns, especially when there is pressure to launch products quickly.
• Uneven standardization: Without uniform regulations, significant differences arise between companies, leading to inequalities and additional risks for users.
• Lack of accountability: Without real sanctions, companies can avoid taking responsibility for the negative effects of their technologies.

The Benefits of External Oversight and Necessary Mechanisms

External regulation does not mean blocking innovation, but creating a safe and predictable framework for AI development. The benefits are clear:

1. Increased transparency: Obligation to report incidents, independent audits, and social impact assessments.
2. Standardization: All actors follow the same rules, reducing risks for users and society.
3. Real sanctions: Penalty mechanisms for violations, discouraging irresponsible behavior.
4. Public trust: Users and partners can trust that AI technologies are developed and operated responsibly.

Conclusion: Lessons and Recommendations for AI Company Responsibility

Experience from high-risk industries is clear: self-regulation is not enough to protect society from the risks and abuses of advanced technologies. AI, with its systemic impact, needs a robust framework of external regulation, with independent oversight mechanisms and real sanctions.

• AI/ML professionals and managers should support regulatory initiatives and actively contribute to defining ethical and technical standards.
• Organizations should see regulation not as a burden, but as an investment in trust, safety, and long-term sustainability.

Responsible progress in AI cannot exist without transparency, external oversight, and accountability. The lessons from aviation and the banking sector are clear: only independent supervision can prevent crises and ensure a safe digital future for all.

(This material was assisted by an AI tool and reviewed by our team before publishing).