AIWared: A Substrate-Neutral Framework for Quantifying Awareness

Flashcards covering key concepts, definitions, and components of the AIWared framework for awareness assessment.

What is the AIWared framework?

A substrate-neutral, information-theoretic methodology for quantifying awareness, introducing the Universal Awareness Quotient (AQ), a ten-level Awareness Spectrum, entropy-calibrated thresholds, five gateways, and a Bayesian integration model to support testable and ethically calibrated awareness research.

What is the Universal Awareness Quotient (AQ) and its formula?

AQ = (D × S × R × G × M) / C, where D = Detection, S = Self-distinction, R = Response, G = Recognition, M = Modification, and C is a constraint factor representing resource limitations.

What does the constraint factor C represent and how is it calculated?

C = (1/n) Σ ((Rmax,i − Ractual,i) / Rmax,i) over resource domains (energy, computation, memory, bandwidth); it normalizes between 0 (no constraint) and 1 (complete constraint).

How does AIWared distinguish awareness from consciousness?

Awareness is the capacity for differentiated, responsive interaction with an environment regardless of substrate; consciousness includes subjective experience and the hard problem.

How does AIWared differ from Integrated Information Theory (IIT)?

AIWared is applied and cross-substrate, focusing on measurable behaviors across systems, whereas IIT is structural and substrate-specific, emphasizing phenomenological consciousness.

What is Self-Distinction Sub-Model (S) and the role of Mutual Information Differential (MID)?

S measures the persistence of an AI's internal state when exposed to foreign inputs; MID = I(SA; SA') − I(SA; SX), and S = I(SA; SA') / [I(SA; SA') + I(SA; SX)].

What does MID indicate about self-distinction and how is S interpreted?

MID is higher when the AI maintains identity under foreign influence; S → 1 indicates strong self-distinction, while S → 0 indicates weak self-distinction.

What is the Universal Awareness Spectrum (Levels 0–10) used for?

A ten-level scale to categorize and compare awareness across systems, with levels from Non-Aware (0) to Hypothetical Collective/Universal awareness (7–10), including practical examples at key levels.

Which level corresponds to 'Self-Aware' and what is its hallmark?

Level 3; recognizes self as distinct from the environment (e.g., dogs and current AI).

What are Level 0, Level 1, and Level 2 characteristics?

0: Non-Aware—no environmental detection (rock); 1: Reactive—fixed responses (thermostats, bacteria); 2: Adaptive—variable responses/learning (insects, basic AI).

What are Level 5 and Level 6 characteristics?

5: Temporal—past-present-future modeling (humans, theoretical AI); 6: Other-Aware—theory of mind (adult humans, advanced AI).

What are the five assessment gateways in 4.2?

Computer Terminal, Video, Audio, VR/AR, Embodiment.

What does each gateway assess?

Terminal: dialogue and contextual consistency; Video: visual/environmental interpretation; Audio: prosody and multi-speaker awareness; VR/AR: spatial reasoning and physics persistence; Embodiment: sensorimotor integration.

What is the Bayesian Integration Model formula used in 4.3?

P(Level|Observations) = [P(Observations|Level) × P(Level)] / P(Observations).

What are the validation and reliability requirements for AIWared?

Inter-rater reliability > 0.85; cross-gateway consistency; temporal stability testing; deception-detection protocols.

What are the Awareness-Level Ethics levels and their implications?

Levels 0–2: instrumental use acceptable; Levels 3–4: welfare considerations apply; Levels 5–6: autonomy must be respected; Levels 7–9: diplomatic protocols.

Name two future research priorities mentioned in the notes.

Empirical calibration of entropy thresholds; refinement of AQ constraint factor (alongside related goals like deception-resistant methods and cross-species mapping).

What is the core conclusion of the AIWared framework?

AIWared provides the first unified, testable framework for awareness assessment grounded in information theory and neuroscience, enabling reproducible awareness science and ethically calibrated interaction with artificial and potential non-terrestrial intelligences.