The Role of Industry Self-Regulation in AI Governance: Lessons from Mobile and Genomics Industries

Why AI Industry Needs Strong Self-Regulation to Thrive in a Laissez-Faire Environment

In light of President Donald Trump’s victory in the 2024 election, with Elon Musk as an ally and strong advocate for reduced federal regulation, questions about the governance of the rapidly evolving artificial intelligence (AI) industry become paramount. This context highlights a critical point: as government regulation of technology and AI may become more laissez-faire, the role of industry-driven self-regulation will be more vital than ever. To better predict the path forward, we can look at lessons from the past—specifically the self-regulation efforts of other industries.

Having had the privilege to witness and contribute to the birth of two entirely new industries—mobile and genomics—through the startups I founded, I reflect on how these sectors shaped self-regulatory frameworks with minimal government intervention. The experiences from both fields can serve as valuable blueprints for navigating the AI challenges.

Lessons from the Mobile Industry’s Self-Regulation

In the early days of the mobile industry, as mobile messaging and apps surged, significant concerns arose about how to maintain user trust and prevent the “spam tragedy” experienced with emails. Questions around mobile security, privacy, and trustworthiness became pressing as mobile devices quickly evolved into powerful communication tools for commerce, or “m-commerce.”

In response, industry leaders and stakeholders came together to form the Mobile Marketing Association (MMA). This self-regulating body, which included telecom companies (T-Mobile was one of the first one to join), content providers (including major brands like Pepsi, Disney, and ESPN), and technology providers, collaborated to develop best practices and guidelines. At PrimeMessage, one of the first SMS Gateway companies, we participated in shaping these foundational rules.

Among these practices were opt-in and opt-out mechanisms for messaging, ensuring consumers understood potential costs associated with text messages, and creating protocols for premium messaging charges. Thanks to these efforts—especially the consumer best practices guidelines we developed and that were widely adopted by telecoms and content providers—you don’t get nearly as many spam texts as spam emails. You’re welcome!

This focus on consumer protection and transparency fostered a healthier industry ecosystem, eventually ensuring trust in mobile commerce. When PrimeMessage was acquired by CellTrust Corporation , our work continued in secure mobile communication, collaborating with Visa and other stakeholders to further standardize mobile security.

MMA's success was also due to highly effective leadership. Laura Marriott played a significant role in forming and establishing global headquarters and regional chapters worldwide which was instrumental in the adoption of MMS's guidelines globally.

Genomics Industry: Establishing Trust and Security Standards

The genomics industry, like AI today, grappled with serious concerns around data privacy and security from the outset. When we launched Silverberry Genomix in 2017, our precision health platform, we immediately confronted questions about protecting sensitive user data. Achieving HIPAA compliance was a baseline step, but further measures were necessary to build a responsible, trusted industry.

To that end, the Global Alliance for Genomics and Health was formed, uniting stakeholders across the spectrum to create best practices for data protection, sharing, and exchange. This collaboration addressed not just data security but also broader questions around privacy, ethical usage, and transparency in the application of genomics. As genomics industry evolves, the Alliance continues to evolve with it by forming new workgroups to stay up to date and responsive to change and the new requirements.

Implications for AI Governance

As AI becomes increasingly embedded in every facet of society the stakes for safety, security, and trustworthiness have never been higher. In an environment where federal regulations may be pared back, organizations like the Coalition for Health AI (CHAI) and the Partnership on AI must play central roles in shaping industry best practices. Much like the mobile and genomics sectors before, this work will need to encompass data privacy, bias mitigation, security protocols, and ethical considerations.

While some may argue for strict governmental oversight of AI, historical precedent shows that industry-driven collaboration can be equally—if not more—effective at setting meaningful standards. Self-regulation offers the advantage of flexibility and rapid adaptation to technological advances, which is critical in fast-moving fields like AI.

Charting the Path Forward

Building a robust self-regulatory framework for AI will require cooperation across the entire ecosystem. This means bringing together technology providers, consumers, researchers, government agencies, and industry alliances to establish transparent, enforceable guidelines. A healthy, self-governed industry is in the interests of all players, including companies, as it ensures a scalable and sustainable industry that benefits everyone. Companies and stakeholders alike will actively participate in making this happen, driven by the mutual benefit of building trust, security, and accountability within the AI space.

Just as the mobile and genomics industries built trust and fostered innovation through self-regulation, the AI community now has the opportunity—and responsibility—to do the same. The Coalition for Health AI and the Partnership on AI have already begun this important work. As an example, in my recent interview at HLTH USA with Merage Ghane, Ph.D. the director for Responsible AI at CHAI, she introduced the scorecard for a trustworthy AI application and the factors it recommends to consider. Their efforts are set to accelerate, creating a future where AI benefits all stakeholders through proactive, collaborative engagement.

Do you have a similar experience in other industries that can be used for AI?

Shayan Mashatian

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