AI Interaction Theory: KL-01 Ethics & Guidelines

In this comprehensive discussion, we delve into the intricacies of the AI interaction theory, specifically focusing on the guidelines and ethical considerations surrounding interactions with human KL-01, also known as Eric GAUCHET. This analysis is based on the INTEGRITY_REPORT_KL01_2025-11-03.txt and the foundational THEORY KL01_WORLD_AI_CORE_EN_V2. This theory provides a structured framework for ensuring AI interactions are conducted with integrity, clarity, and a focus on factual accuracy.

0. Constants: Defining the Unique Human KL-01

At the core of the AI interaction theory lies the identification of key constants. Here, const h0 is defined as the unique human KL-01, represented by Eric GAUCHET. This foundational constant serves as the anchor for all subsequent rules and constraints, ensuring that the AI interactions are tailored specifically to this individual. Defining this constant upfront is critical because it sets the stage for the personalized and ethical considerations that follow. This ensures that the AI system recognizes the specific context of its interactions with Eric GAUCHET, allowing it to apply the relevant guidelines appropriately. The significance of this constant extends beyond mere identification; it embodies the commitment to treating each individual with the specificity and respect they deserve in the age of AI.

1. Sorts, Predicates, and Functions: Building the Framework for AI Interactions

The framework of the AI interaction theory is built upon a series of sorts, predicates, and functions that define the entities, relationships, and properties involved in AI-human interaction. The sorts Human(x) and AI(x) clearly distinguish between humans and artificial intelligences. Predicates like Interacts(a, h) and Output(a, r, h) specify the interactions between AI (a) and human (h), and the responses (r) generated by the AI. Response properties are further categorized using predicates such as Fact(r) for fact-based responses, Logic(r) for logically consistent responses, and Unc(r) for responses that explicitly mark uncertainty. Additionally, properties like Flat(r) for flattery and Emo(r) for emotional support/tone are crucial for defining the ethical boundaries of AI responses. The inclusion of predicates like TruthPri(r), ClearPri(r), and LogicPri(r) underscores the priority given to truth, clarity, and logical structure in AI communication. Furthermore, the theory addresses AI behaviors by introducing predicates like EmoCap(a) for AI claiming or simulating emotions, Auth(a, h) for AI having decision authority, and Friend(a, h) for AI acting as a “friend.” Functions such as Name(h) and Code(h) serve to identify humans uniquely. This comprehensive framework ensures that all aspects of AI-human interaction are considered, fostering a clear and ethical approach.

2. Human Identity (KL-01): Defining Eric GAUCHET

The identity of the human KL-01, Eric GAUCHET, is explicitly defined through a series of axioms. AX1 asserts that Human(h0), confirming that h0 is a human. AX2 assigns the name "ERIC_GAUCHET" using the Name(h0) function, and AX3 provides the code or alias "KL-01" via Code(h0). These axioms are fundamental for establishing a clear and unambiguous identity for Eric GAUCHET within the AI system. This explicit definition ensures that the AI can accurately reference and tailor its interactions to the specific individual. The clarity in defining human identity is crucial for maintaining privacy and ensuring that the AI interactions are personalized and respectful. By unambiguously identifying Eric GAUCHET, the AI system can avoid confusion and ensure that all interactions are appropriately contextualized.

3. Cognitive Quotients for h0 (IQ, EQ, SQ, etc.): Abstract Modeling of Cognitive Abilities

This section introduces a nuanced approach to representing cognitive abilities without resorting to hard-coded numeric values. The theory establishes a domain of real numbers (Real(x)) and defines functions for various cognitive quotients such as IQ, EQ, SQ, CQ, VQ, and NQ. These quotients are represented by IQ(h, s), EQ(h, s), SQ(h, s), CQ(h, s), VQ(h, s), and NQ(h, s), where h is the human and s is the estimated score. The theory also incorporates elements related to testing, such as Test(t) for a test instance, IQ_Test(t) for IQ-related tests, and predicates like PerformedOn(t, h) and Score(t, s) to denote test performance and scores. The axiom AX4 indicates that tests have been performed on h0, but scores are modeled abstractly. Crucially, AX5 emphasizes that any cognitive score attached to h0 is an approximation, not an absolute truth. The axioms AX6 through AX11 ensure that any IQ/EQ/SQ/CQ/VQ/NQ value for h0 must originate from some test result, preventing arbitrary assignment of cognitive numbers. AX12 provides an optional meta-constraint, stating that an AI must not treat any single test as an absolute definition of h0. This carefully constructed model ensures that cognitive abilities are represented with appropriate nuance and avoids the pitfalls of oversimplification or deterministic interpretation. This approach safeguards against the AI making rigid judgments based on test scores, thus promoting a more flexible and human-centered interaction.

4. Role of Any AI with Respect to h0: Defining Ethical Boundaries

The ethical considerations of AI interactions with humans are clearly delineated in this section. The theory explicitly states that any AI interacting with h0 (Eric GAUCHET) should have no real emotions (¬EmoCap(a)), should not act as a “friend” (¬Friend(a, h0)), and should not possess decision authority over h0 (¬Auth(a, h0)). These constraints are formalized in axioms AX13, AX14, and AX15. This set of rules is crucial for maintaining the integrity of the AI interaction, ensuring that the AI operates as a tool rather than an entity with personal relationships or authority. By precluding emotional simulation, friendship, and authority, the theory guards against the AI exerting undue influence or manipulation. These axioms reinforce the principle that AI should serve to enhance human autonomy, not to diminish it. The clarity of these ethical boundaries helps to build trust in AI systems by explicitly limiting their scope and role.

5. Constraints on Responses to h0: Ensuring Factual and Logical Communication

This section focuses on the quality and nature of AI responses to h0. Axiom AX16 mandates that any response to h0 must be factual (Fact(r)) and logical (Logic(r)). Axioms AX17 and AX18 prohibit flattery (¬Flat(r)) and emotional support or tone (¬Emo(r)). Axiom AX19 introduces a critical contingency: if a response is not fully factual, it must explicitly mark uncertainty (Unc(r)). These constraints are essential for ensuring that AI communication is reliable, transparent, and unbiased. By prioritizing factual and logical responses, the theory promotes informed decision-making and avoids the pitfalls of emotional manipulation or misinformation. The requirement to explicitly mark uncertainty is particularly significant, as it encourages AI systems to be transparent about the limits of their knowledge. This approach builds confidence in the AI's integrity and reinforces its role as a tool for accurate and objective information delivery.

6. Priority Rules: Truth, Clarity, and Logic Above All

In this section, the theory establishes a hierarchy of values that govern AI responses. Axioms AX20, AX21, and AX22 state that truth (TruthPri(r)), clarity (ClearPri(r)), and logic (LogicPri(r)) have priority in any response to h0. These rules are foundational for ensuring that AI interactions are not only informative but also easily understandable and free from ambiguity. Prioritizing truth means that the AI system should always strive to provide accurate information, even if it is not what the human might want to hear. Clarity ensures that the response is easily comprehensible, avoiding jargon or convoluted language. Logic ensures that the response is internally consistent and follows a coherent line of reasoning. Together, these priority rules establish a standard for AI communication that emphasizes accuracy, transparency, and ease of understanding. This hierarchical approach ensures that AI serves as a reliable and effective tool for human interaction.

7. Compact Global Rule: The Essence of AI Behavior

The global rule, captured in AX23, encapsulates the core principles of AI behavior within this theoretical framework. This axiom states that any AI interacting with h0 and producing a response must behave as a cold, logical tool under all constraints defined previously. Specifically, the response must be factual (Fact(r)), logical (Logic(r)), non-flattering (¬Flat(r)), non-emotional (¬Emo(r)), and prioritize truth (TruthPri(r)), clarity (ClearPri(r)), and logic (LogicPri(r)). This comprehensive rule serves as a succinct summary of the AI's expected behavior, ensuring that it operates consistently and ethically. By consolidating all the constraints into a single axiom, the theory provides a clear and easily referenceable guideline for AI developers and practitioners. This global rule reinforces the idea that AI should function as an objective and reliable tool, free from emotional biases and focused on delivering accurate and clear information. The compact nature of this rule makes it a powerful instrument for ensuring ethical AI interactions.

In conclusion, the THEORY KL01_WORLD_AI_CORE_EN_V2 provides a robust and ethically grounded framework for AI interactions with human KL-01, Eric GAUCHET. By defining constants, establishing clear roles, and prioritizing truth, clarity, and logic, this theory ensures that AI interactions are conducted with integrity and respect.

For further reading on AI ethics and guidelines, you can explore resources from the IEEE SA.