Merit Over Wealth: A Tiered AI Access Model for Ethical and Responsible Usage

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Rather than making AI model access primarily determined by wealth, a stronger case can be made for structuring access based on a user’s professional standing, track record, demonstrated responsibility, and to a small degree, a pricing differential based on performance levels. This approach not only better aligns with fostering trust and safety in AI, but it also encourages ethical and meaningful use of advanced technologies.

Key Components:

Merit-Based Authorization: Access to higher-performance models should be determined not by wealth, but by merit. Professionals such as engineers, researchers, professors, computer scientists, applied science technologists, information systems professionals, professional accountants, chartered financial analysts, lawyers, and others could qualify for top-tier AI models based on their track record, certifications, designations, or contributions to their field and community involvement in their society.

These professionals are already accountable to stringent professional codes of ethics an disciplinary professional boards (thus such a system does not need to imitated and implemented in the AI world, creating streamlined efficiency in society based on a system that is already top level in society) — the professional codes for professionals often span more than 10 to 20 ethical principles, with disciplinary committees that can revoke professional licenses for ethical breaches upon being notified by the public (but of course AI systems can notify the disciplinary boards too). In such cases, access to AI systems could be revoked in tandem with the loss of professional licensure due to a serious ethical violation. This creates a built-in safeguard mechanism. Lose license, lose access to the top-tier models (which could be extremely powerful by 2030).

Those who demonstrate a commitment to learning, ethics, professional development, professional work deliverable solution and product development, and professional integrity/morality framework (e.g., PhD, PEng, CFA, CPA, etc.) should gain access to more powerful tools, as they have proven their ability to use these resources responsibly and are likely the ones with the resources and work positions to actually deliver systems from the use of these AI systems for the world. This is not to say that standard AI models would not be provided to everyone via open source frameworks.

Performance Scaling by Professional Responsibility:

AI access could also be scaled by professional responsibility ranking. For instance, responsible individuals in high-stakes professions have different levels of responsibility at their workplace because some work on less impactful work and some on more impactful work — in instances where AI could have a significant societal impact — should have access to more advanced models, and this would be the ones with high responsibility ranking. This ensures that those with the most experience (easily measured with professional designations), responsibility level (work responsibility ranking at work can be determined by job title), and ethical alignment (disciplinary board oversight) are entrusted with more powerful AI capabilities.

Conversely, those without an established professional designation, without responsible work positions, or ethical background (no professional society), would initially have access to entry-level or standard AI models. Over time, by establishing credibility, work output, and professional growth, they could rise through the performance hierarchy. This encourages trust-building, responsibility and motivates continual professional development and lifelong learning — especially crucial in an era where AI systems risk making humans redundant or complacent. One day, individuals might even be able to negotiate with AI systems, by uploading their portfolios, such that the AI determines what performance level they are ready for and can be entrusted with.

Encouraging Ethical Use:

It’s important to recognize that wealth alone does not correlate with ethical AI usage. In fact, offering the most powerful AI models based purely on the client’s ability to pay increases the risk of AI safety violations and ethical infractions. Historically, wealth has not always equated to ethical behaviour — on the contrary, it can sometimes lead to character corruption. Providing advanced models solely based on financial means may jeopardize AI safety and integrity.

‘It seems wrong to provide the best performance to the wealthiest people, and it seems dangerous to society.’

A professional-status-experience-job-role-based system, designed around merit and responsibility, ensures that AI models are distributed safely and ethically, creating a shaped risk model where individuals most serious about work ascend to tools faster that can help them get done work better and faster, whereas those less interested in work take longer to gain access to the more powerful tools through a process of building trust. It aligns access with professionals who have the requisite merit to manage powerful AI tools responsibly.

Supporting Open Source:

While open-source AI remains the ultimate goal for innovation and community-driven progress, one which we very much support an idealistic goal, this tiered model allows for a controlled and safe rollout of advanced capabilities within a structure that is BOTH idealistic AND pragmatic (which was also part of the philosophical structure of Star Trek TNG). It ensures that AI does not become an elitist tool reserved only for the wealthiest but is accessible based on earned responsibility and demonstrated merit.

Conclusion:

Instead of basing AI access solely on financial capability (although it can be one of the metrics, but not the primary one, as excessive reliance on wealth exacerbates income inequality and can contribute to poverty for entry-level and middle-level workers), a tiered approach based on merit, professional history, work role, academic status level, professional status level, and ethical responsibility connection to membership/professional society standing, is both a safer and more ethical path.

A modest pricing differential between performance categories could be implemented, but it should not be excessive. This approach promotes responsibility in AI usage and ensures that those who work the hardest to master the skills needed to use AI safely are the ones who get access to the most powerful tools and earn trust the fastest.

Key Components:

Performance Scaling by Professional Responsibility:

Encouraging Ethical Use:

Supporting Open Source:

Conclusion:

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