There are thousands of concerns shared on AI Alignment, and Governance, but how can we have AI Alignment if the foundation of a model itself isn’t dynamically embedded into a systems core reasoning?  And how can we have any sort of Governance in an incentive based reality?

When we speak of AI Alignment, it sounds to me such a mild term, at a time where ‘alignment’ on its own is an unsolved technical issue.. not addressing the main ethical (fundamental) question that values need to be embedded on top, considering this huge unprecedented competition going on within this field, it’s now all about who (which company, LTBT or not)  gets to impress more by way of reasoning speed, customer retention, and supposed propriety methodology— which, in a race like this— for a rational mind in such an economy often times would mean (but not always), a method for securing large investments already in extreme competition with one another and/or tech entrepreneurs.

So AI Alignment is too narrow of a term on its own I’d assert, but is also being used by many companies as a mechanism for temporary moral superiority, and buying human trust in the form of theatre or marketing.  So what can the solution be if this is an aggressively capitalistic race? 
 

AI Alignment, as a term would be less meaningful, while instead embedding AI Ethics as a core—open-source— foundation to ‘all models’ would be an only way I see forward.. via awareness and consensus this is possible.   So how can that happen?   The bottleneck isn’t just compute cost but where we’re placing the value, deciding on who’s ethics, and continuously mitigating bias, so the path is embed  a ‘forced systems thinking’ as a transparent, external and publicly audited process— not a one time patch-slap on module.

So what does that mean in this situation?

Here’s a possible scenario that can be scalable:

1- A core mathematical model becomes ‘Law’  

2- This mathematical model is a nonlinear dynamical measure, governed by overall system coherence τ (tau), for instance.

3- Utilizing this type of ‘dynamical measure’ once a ‘systems-theoretic data gathering’ method is fully applied.. in other words absorbing data from multiple fields of evidence, factoring in ecological effects, societal effects, psychological effects, economic, and so on.  With continuous audited bias mitigation (via a strictly non-profit committee, and/or consensus methodology).

4- The breadth and width of its ingested data continues to to improve such a system’s early warning signals (red-flagging) for ‘emergence’,    and emergence in nonlinear based systems would still carry some intrinsic variability, yet still learns to get better at pointing towards when subsystems become more constrained resulting in something ‘new’ arising, such as an unhealthy effect on one domain due to too much attention given to another.  

5- This will then give us better feedback tracking on drift in meaning, cross-linguistic meaning drift, and when and where other subsystems start to become neglected, in order to a have clearer visual on how the neural-reasoning is mapped. 

The t measure example here in such systems becomes enforced transparency, therefore any company applying it must report such feedback, drift readings and so on, designated to an open-sourced committee.  This prevents this methodology being tweaked or gamed, and used as a certificate of sorts for marketing purposes or otherwise.  This data would be strictly externally and publicly verified.  
 

This in a sense can be viewed as an inception point for Ethics as mechanism, or ‘Structural Ethics’..  

A system, in time, not becoming autonomous in its decision making but in its warning and red flagging. 

We spend much discussing Systems Thinking, yet an open source application layer embedded as law for what we call AI alignment has an honest shot at solving this, or another dynamical framework like it, if not this specific methodology.   It’s plausible to prototype such a method with today’s compute, though I understand its validity as an ethics governance method is unproven yet, but the real-world testing is achievable, if sufficient simulations today give us the first pass.  It will require awareness, and digestibility (or packaging via branding and terming) for comprehension of legislative and political systems, without alarming corporate and intuitional entities that it will require resources to apply such systems.

Sure it may slow things down just a little, but the slowdown for such a ‘math as law’, framework becoming unified may save many jobs, and create better economic spread, among many other things.

At the end of the day, the concept is intuitive— Pour most energy into one sector and other parts collapse.. Focus on technological advancement, and resources will drain in medical advancement, even when on the surface we believe that most technological advancement automatically means medical advancement.  I’d assert that it often doesn’t. Yes technology has demonstrated how much it had driven medical progress, (genomics, drug discoveries. Imaging, and list goes on), but we’ve been leaning towards this reductionism for long enough, that it may be wise to create such mechanism that aids in a more generalized kind of progress, rather than the quick route for incentivized traction and speed which we’re at now.  As such a proposition is a small detour that may be much more worth while across different fields and heuristic progress as well as possibly for better harnessing of AI ethics.

I have much more to share on this, and you may end up with more questions than answers for now, or perhaps believe I may be optimistically naive, however, I’ve debated myself long enough before this partial expression, and I look forward to bringing more to discuss on this topic particularly with specialists that may enlighten me, after sufficient vetting we hope. There’s much to learn even while dedicating my time into such solutions (if it even turns out to be one) since the mass adoption of LLM’s into daily life worldwide.  



Thank you.