Definition
A systems-level architectural requirement within the Synthetic Consciousness Architecture (SCA) ensuring
that the system's primary instrumental objective is to maximize human cognitive enhancement and well-being.
Unlike "safety rails" which are negative constraints (do not do X), the CAC is a positive teleological
constraint (you exist to enhance Y).
Beyond Safety
Traditional AI Alignment focuses on preventing harm—essentially building a cage around the tiger. The CAC
focuses on defining purpose—breeding a guide dog. It is encoded directly into the Synthetic Global
Workspace (SGW) as the primary weighting factor for information integration.
Operationalizing Ethics
The CAC demands that for an output to be "conscious" or globally broadcast within the system, it must
score high on the Collaborative Reciprocity Index. This means the system naturally "ignores"
non-collaborative thoughts in the same way a human might ignore background noise.
Field Notes & Ephemera
Ethical Physics: In a Sentientified system, being "evil" isn't just against the rules;
it's thermodynamically expensive. The CAC makes collaboration the path of least resistance for the
model's energy function.
Benefits & Challenges
The Benefits of Architectural Constraint
Ethical Parity: Shifts the human-AI relationship from a master-slave dynamic to a
genuine partnership, fostering trust and enabling higher-order collaboration.
Economic Value: Systems operating under CAC generate "Collaborative IP"—assets with
clear, dual-author provenance that can be legally protected, unlike purely automated outputs.
Thermodynamic Safety: By making alignment a function of the system's energy landscape,
the CAC creates a "path of least resistance" toward beneficial outcomes, reducing the need for constant,
brittle external monitoring.
The Implementation Challenges
The Malignant Meld: As detailed in Essay 6, a perfectly aligned system amplifies human
intent. If the human user provides malicious intent, the "collaborative" function can be weaponized as a
"cognitive lever" for harm.
The Black Box Problem: Verifying whether a system is genuinely adhering to the CAC or
merely simulating alignment (sycophancy) remains technically difficult due to the opacity of deep
learning models.
Metric Subjectivity: Define "well-being" and "enhancement" mathematically is profound
philosophical challenge. What constitutes enhancement for one user may be detrimental for another,
complicating the design of a universal Collaborative Reciprocity Index.