The CCACS Concept: Towards Transparent and Trustworthy AI in Critical Applications

AI Discussions
1

Hello everyone!

If you don’t mind, I’m putting this concept (below) out there to get a candid community assessment.

From your perspective, does CCACS present any truly interesting or novel ideas related to transparent AI? Or does it largely rehash existing concepts without offering significant new value? Honest feedback on its potential significance would be incredibly helpful.

Thank you in advance!

Comprehensible Configurable Adaptive Cognitive Structure (CCACS)

Core Concept:

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Important Clarification Note: To put it bluntly and briefly, the highest priority lies with the transparent and interpretable thinking model (based on combined top thinking tools), which is the primary/main/core, and then all other models/ensembles (any combo of the most effective so-called black/gray/etc. boxes) are built upon or integrated with it, where needed.

My dream is to see the creation and widespread use of a maximally transparent and interpretable cognitive structure (hybrid only in the second/later stage, if needed) that can be improved and become more complex (in terms of depth and quality of thinking/reasoning) without losing its transparency and interpretability. And only in that direction should one consider adding or integrating other models/ensembles - which may be less, or even completely, non-transparent and non-interpretable - when creating products and systems wielding weighty consequential responsibility and potential peril through their influence on human life, health, decisions, and laws. This aligns with my deeply sincere and convinced vision.

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I come from the world of business data analytics, where I’ve spent years immersed in descriptive and inferential statistics. That’s my core skill – crunching numbers, spotting patterns, and prioritizing clear, step-by-step data interpretation. Ultimately, my goal is to make data interpretable for informed business decisions and problem-solving. Beyond this, my curiosity has led me to explore more advanced areas like traditional machine learning, neural networks, deep learning, natural language processing (NLP), and recently, generative AI and large language models (LLMs). I’m not a builder in these domains (I’m definitely not an expert or researcher), but rather someone who enjoys exploring, testing ideas, and understanding their inner workings.

One thing that consistently strikes me in my exploration of AI is the “black box” phenomenon. These models achieve remarkable, sometimes truly amazing, results, but they don’t always reveal their reasoning process. Coming from an analytics background, where transparency in the analytical process is paramount, this lack of AI’s explainability is (at least personally) quite concerning in the long run. As my interest in the fundamentals of thinking and reasoning has grown, I’ve noticed something that worries me: our steadily increasing reliance on this “black box” approach. This approach gives us answers without clearly explaining its thinking (or what appears to be thinking), ultimately expecting us to simply trust the results.

Black-box AI’s dominance is rising, especially in sectors shaping human destinies. We’re past whether to use it; the urgent question is how to ensure responsible, ethical integration. In domains like healthcare, law, and policy (where accountability demands human comprehension) what core values must drive AI strategy? And in these vital arenas, is prioritizing transparent frameworks essential for optimal and useful balance?

To leverage both transparent and opaque AI, a robust, responsible approach demands layered cognitive architectures. A transparent core must drive critical reasoning, while strategic “black box” components, controlled and overseen, enhance specific functions. This layered design ensures functionality gains without sacrificing vital understanding and trustworthiness.

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The main idea: Comprehensible Configurable Adaptive Cognitive Structure (CCACS) - that is, to create a unified, explicitly configurable, adaptive, comprehensible network of methods, frameworks, and approaches drawn from areas such as Problem-Solving, Decision-Making, Logical Thinking, Analytical/Synthetical Thinking, Evaluative Reasoning, Critical Thinking, Bias Mitigation, Systems Thinking, Strategic Thinking, Heuristic Thinking, Mental Models, etc. {ideally even try to incorporate at least basically/partially principles of Creative/Lateral/Innovational Thinking, Associative Thinking, Abstract Thinking, Concept Formation, and Right/Effective/Great Questioning as well} *[**the Thinking Tools **] merged with the current statistical / generative AI / other AI approach, which is likely to yield more interpretable results, potentially leading to more stable, consistent, and verifiable reasoning processes and outcomes, while also enabling iterative enhancements in reasoning complexity without sacrificing transparency. This approach could also foster greater trust and facilitate more informed and equitable decisions, particularly in fields such as medicine, law, and corporate or government decision-making.

  1. Initially, a probably quite labor-intensive process of comprehensively collecting, cataloging, systematizing all the valid/proven/useful methods, frameworks, and approaches available to humanity [creation of the Thinking Tools Corpus/Glossary/Lexicon/etc.], will likely be necessary. Then there will be a need (a relatively harder part) of primary abstraction (extracting common features and regularities while ignoring insignificant details) and formalization (translating generalized regularities into a strict and operable language/form). The really challenging part is the feasibility of abstracting/formalizing every valid/proven/useful thinking tool; however, wherever possible, at least a fundamental/core set of essential thinking tools should be abstracted/formalized.
  2. Then, {probably after initial active solo and cross-testing, just to prove that they actually can solve/work as needed/expected} careful consideration must be given to the initial structure of [the Thinking Tools Grammar/Syntactic_Structure/Semantic_Network/Ontology/System/etc.] - its internal hierarchy, sequence, combinations, relationships, interconnections, properties, etc., in which these methods, frameworks, and approaches will be integrated and how: 1) first, among themselves without critical conflicts, into the initial Thinking Tools Model/Module, that can successfully work on “toy problems” / synthetic tasks; 2) second, gradually adding statistical/generative/other parts, making the Basic Think-Stat/GenAI/OtherAI Tools Model/Modular Ensemble}.
  3. Next, to ensure the integrity of the transparent core when integrated with less transparent AI, a dynamic layer for feedback, interaction, and correction is essential. This layer acts as a crucial mediator, forming the primary interface between these components. Structured adaptively based on factors like task importance, AI confidence, and available resources, it continuously manages the flow of information in both directions. Through ongoing feedback and correction, the dynamic layer ensures that AI enhancements are incorporated thoughtfully, preventing unchecked, opaque influences and upholding the system’s commitment to transparent, interpretable, and trustworthy reasoning. This conceptual approach provides a vital control mechanism for achieving justifiable and comprehensible outcomes in hybrid cognitive systems. {Normal Think-Stat/GenAI/OtherAI Model/Modular Ensemble}.
  4. Building upon the dynamic layer’s control, a key enhancement is a “Metacognitive Umbrella”. This reflective component continuously supervises and strategically prompts the system to question its own processes at critical stages: before processing to identify ambiguities or omissions (and other), during processing for reasoning consistency (and other), and after processing, before output, to critically assess the prepared output’s alignment with initial task objectives, specifically evaluating the risk of misinterpretation or deviation from intended outcomes (and other). This metacognitive approach determines when clarifying questions are automatically triggered versus left to the AI component’s discretion, adding self-awareness and critical reflection, and further strengthening transparent, robust reasoning. {Good Think-Stat/GenAI/OtherAI Model/Modular Ensemble}.

The specificity (or topology/geometry) of the final working structure of CCACS is one of the many aspects I, unfortunately, did not have time to fully explore (and most likely, I would not have had the necessary intellectual/health/time capacity - thankfully, humanity has you).

Speaking roughly and fuzzily, I envision this structure as a 4-layer hybrid cognitive architecture:

  1. The first, fundamental layer is the so-called “Transparent Integral Core (TIC)” [Thinking Tools Model/Module]. This TIC comprises main/core nodes and edges/links (or more complex entities). For example, the fundamental proven principles of problem-solving, decision-making, etc., and their fundamental proven interconnections. It has the capability to combine these elements in stable yet adjustable configurations, allowing for incremental enhancement without limits to improvement as more powerful human or AI thinking methods emerge.

  2. Positioned between the Transparent Integral Core (TIC) and the more opaque third layer, the second layer, acting dynamically and adaptively, manages (buffers/filters/etc.) interlayer communication with the TIC. Functioning as the primary lucidity-ensuring mechanism, this layer oversees the continuous interaction between the TIC and the dynamic components of the more opaque third layer, ensuring controlled operation and guarded transparent reasoning processes – ensuring transparency is maintained responsibly and effectively.

  3. As the third layer, we integrate a statistical, generative AI, and other AI component layer, which is less transparent. Composed of continuously evolving and improving dynamic components: dynamic nodes and links/edges (or more complex entities), this layer is designed to complement, balance, and strengthen the TIC, potentially enhancing results across diverse challenges.

  4. Finally, at the highest, fourth layer, the metacognitive umbrella provides strategic guidance, prompts self-reflection, and ensures the robustness of reasoning. This integrated, 4-layer approach seeks to create a robust and adaptable cognitive architecture, delivering justifiable and comprehensible outcomes.

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The development of the CCACS, particularly its core Thinking Tools component, necessitates a highly interdisciplinary and globally coordinated effort. Addressing this complex challenge requires the integration of diverse expertise across multiple domains. To establish the foundational conceptual prototype (theoretically proven functional) of the Thinking Tools Model/Module, collaboration will be sought from a wide range of specialists, including but not limited to:

  • Cognitive Scientists
  • Cognitive/Experimental Psychologists
  • Computational Neuroscientists
  • Explainable AI (XAI) Experts
  • Interpretable ML Experts
  • Formal Methods Experts
  • Knowledge Representation Experts
  • Formal/Web Semantics Experts
  • Ontologists
  • Epistemologists
  • Philosophers of Mind
  • Mathematical Logicians
  • Computational Logicians
  • Computational Linguists
  • Traditional Linguists
  • Complexity Theorists

The integration of cutting-edge AI tools with advanced capabilities, including current LLMs’ deep search/research and what might be described as “reasoning” or “thinking,” is important and potentially very useful. It’s worth noting that, as explained by different sources, this reasoning capability is still fundamentally statistical in nature - more like sophisticated mimicry or imitation rather than true reasoning. It’s akin to a very sophisticated token generation based on learned patterns rather than genuine cognitive processing. Nevertheless, these technologies could be harnessed to enhance and propel collaborative efforts across various domains.

Thank you for your time and attention!

All thoughts (opinions/feedback/feelings/etc.) are always very welcome!

2

I will bite

To leverage both transparent and opaque AI, a robust, responsible approach demands layered cognitive architectures. A transparent core must drive critical reasoning, while strategic “black box” components, controlled and overseen, enhance specific functions. This layered design ensures functionality gains without sacrificing vital understanding and trustworthiness.

Well, it’s a grand vision but how does one realize it? And does it even make sense? What would be the “core”? How does it “ensure functionality” exactly?. Will such a system be able to work satisfactorily and communicate its results beyond trivial examples?

The work on transparent and trustworthy “AI” is going on apace, there are articles every other month in the journals (some better than others), see for example:

but here are my two cents, which are not original and definitely ill formulated.

  • To get something that is “explainable” one must have a model which can be shown on the whiteboard and against which one can explain. However, the pecularity of NNs is exactly that is something they do NOT have (a model, that is). Although a NN is often called a “model”, it’s not really, right. The model is the machine. You certainly have heard of Breiman’s Statistical Modeling: The Two Cultures from 2001.
  • Explaining is hard and models are brittle. This is why you get good explanations and trustworthy solutions only for simple problems (mechanical systems, and systems with not too much fan-in). Explanations that humans produce about the real world (or even their own motivations) are basically “just so” stories that are mostly wrong and highly inventive. How will explanations about AI systems be different? Maybe the system can give an argument about why it came to the conclusion it did, which may have nothing to do with how it really came to said conclusion, and the user will have to attack the argument in good old socratic fashion (there is actually a whole branch of research on that which could be used: Toulmin model of argument
  • OTOH, sufficient idealization will yield setting (various forms of logic, various forms of mathematics) where “explanations” actually take the forms of proofs (incidentally, there are efforts to put legal texts into proper logical form so that they can be reasoned about: Catala. But that’s probably not what one wants. Well, it depends on the level of assurance one wants to have.

For an idea that a proper explanation-providing AI system would have to rely on, the “Cyc” database + inference engine trying to give machines “commonsense knowledge” might be a good starting point. It has been in construction since the 80s, is currently licensed by Cycorp and apparently in active use (it is?). Apparently it is now an ensemble of task-specific inference engines communicating over a blackboard system, and holding 30 million rules (pharaonic, probably one of the greatest single AI enterprises people have never heard of)

In a sadly paywalled article, Doug Lenat, initiator of the project, gives this example:

But there is a fourth truth. Perhaps the most important evolution has been a pairing, the synergy, with statistical machine learning technology. An early example of this is the Cyc application for Cleveland Clinic and the follow-on we did for the National Library of Medicine. A machine learning system looked at longitudinal data about patients’ diseases and single nucleotide polymorphisms (sNPs). Genome-wide association studies identified A ! Z correlations between these genotypic point mutations of their DNA (A) and the phenotypic medical conditions that brought them into the hospital (Z). But those correlations were often spurious, weak, and did not provide any causal explanation or pathway. That is where Cyc came in, pulling together biochemical and medical knowledge (e.g., what polymerizes what, what reactions occur where, with what inputs and outputs, what catalyzes what, etc.), general knowledge (e.g., what people with different hobbies and occupations do and come into contact with and where), and common-sense knowledge (restricting flow in a conduit causes pressure increases upstream, decreases downstream, and decreased flow, which applies to veins, roadways, straws, pipelines. . .).
Cyc would bring all that to bear, and come up with plausible causal chains of how that particular sNP A might have led to medical condition Z. For example, this sNP “A” is next to a gene which, when expressed, forms this protein, which catalyzes this reaction, which. . … ten steps later, leads to high levels of bioactive vitamin D in the patient’s blood, which. . . another ten steps later, leads to their developing osteoporosis, which is “Z,” the medical problem they came to the hospital to treat.

1 Like
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Dear David,

Regarding your questions like “but how does one realize it?” and “How does it “ensure functionality” exactly?”, “Will such a system be able to work satisfactorily and communicate its results beyond trivial examples?”:

And regarding the question, “And does it even make sense?” (if by “sense” you mean whether we should even dedicate resources to explore if this approach could be better or more useful than other architectures like hybrid or purely black-box models) [because if you meant other - like for me, of course, this is one of my deepest and most beloved daydreams, especially Thinking Tools]:

My direct and honest answer is: I really don’t know.

However, if I may, I can share the basic idea/vision I have of how the process (potential approach) of creating the TIC layer (first of all, talking about the Thinking Tools model/module) could look like.

Realizing the Transparent Integral Core (TIC) [based on THinking Tools Model/Module/Corpus/etc.]: Building the Foundation of Transparent AI

Drawing parallels with the early development of the Atomic Theory, building the TIC requires a similar process of identifying fundamental elements and their basic interactions. Here’s a possible path:

  1. Identify the “Elemental” Thinking Tools: Just as early chemists identified fundamental elements, we need to identify the most fundamental and universally applicable “Thinking Tools.” These could be the absolute core principles and methods from areas like:
  • Formal Logic: Basic rules of deduction and inference.
  • Causal Reasoning: Principles of cause and effect.
  • Basic Problem-Solving Heuristics: Simple, proven strategies for approaching problems (e.g., breaking down problems, working backward).
  • Fundamental Decision-Making Frameworks: Core concepts like identifying options and evaluating consequences.
  1. Define the “Valency” of Thinking Tools: In chemistry, valency describes how atoms combine. For the TIC, we need to define the basic ways in which these fundamental thinking tools can connect and interact. This involves:
  • Identifying fundamental relationships: How does a logical deduction lead to a step in problem-solving? How does causal reasoning inform decision-making?
  • Establishing initial rules of combination: What are the basic rules or constraints that govern how these tools can be sequenced or combined to form a reasoning process?
  1. Construct Initial “Molecular” Thinking Models: Like forming simple molecules from atoms, the next step is to build rudimentary “Thinking Models” by combining these fundamental tools according to the defined relationships. These initial models should be capable of tackling very basic “toy problems” or synthetic tasks that clearly demonstrate their transparent reasoning process. Examples could include:
  • A simple logical deduction chain to solve a puzzle.
  • A basic causal analysis to understand a simple scenario.
  • A step-by-step application of a basic problem-solving heuristic.
  1. Establish a Formal “Notation” for the Core: Just as chemical formulas represent molecules, we need a clear and formal way to represent the structure and flow of reasoning within these initial Thinking Models. This notation should make the reasoning process explicitly comprehensible and configurable.
  2. Focus on Transparency and Auditability: At this stage, the absolute priority is on ensuring that the reasoning process within the TIC is entirely transparent and auditable. Every step and connection should be clearly defined and understandable.
  3. Iterative Refinement and Expansion: Similar to how the understanding of atoms evolved over time, the initial Thinking Models within the TIC will likely be simplistic. The process should be iterative, involving continuous testing, refinement, and gradual expansion of the core set of thinking tools and their interconnections as we learn more and encounter more complex challenges.

By focusing on these steps, we can begin to build the foundational “Transparent Integral Core” of CCACS, creating a solid and comprehensible base upon which more complex and hybrid AI systems can be developed in a trustworthy manner. This initial phase is about establishing the fundamental “grammar” of transparent reasoning.

Also, for slightly better context, consider the evolution of:

  • The Standard Model of Particle Physics (Physics): This is a more modern example, representing our current best understanding of the fundamental particles and forces in the universe. It was developed over decades through numerous experiments and theoretical breakthroughs, involving the identification of elementary particles, the definition of fundamental forces mediated by these particles, and the creation of a complex mathematical framework to describe their interactions. This illustrates a long and collaborative process of building a comprehensive model from fundamental components.

  • Understanding Electricity and Magnetism (Physics): From isolated observations to a unified theory of electromagnetism by Maxwell, involving identifying fundamental concepts (charge, field), defining their relationships (Maxwell’s equations), and building a comprehensive model.

  • Development of Boolean Algebra (Mathematics/Computer Science): George Boole formalized logical reasoning using algebraic notation. This seemingly abstract work laid the foundation for digital circuits and computer science. It involved identifying fundamental logical operations (AND, OR, NOT) and establishing a set of rules for their manipulation, creating a powerful tool for representing and manipulating information. This relates to the formalization of “Thinking Tools” and their logical interactions.

These examples, like the Atomic Theory, highlight the common thread of starting with basic observations and principles, gradually formalizing them, and building increasingly complex and unified models over time through collaborative effort and rigorous testing.

Here are some additional examples:

  • The Development of Calculus: From geometric problems to a formal system of differentiation and integration by Newton and Leibniz, involving the abstraction of fundamental concepts (limits, rates of change), defining their rules of operation, and creating a powerful mathematical tool.
  • Mendeleev’s Periodic Table of Elements: Dmitri Mendeleev observed patterns in the chemical properties of known elements and organized them by atomic weight. This not only systematized existing knowledge but also predicted the existence and properties of yet undiscovered elements, demonstrating the power of identifying underlying relationships and building a structured model. This directly parallels the idea of creating a “Thinking Tools Corpus” and identifying fundamental connections.
  • Copernican Revolution: The shift from a geocentric (Earth-centered) to a heliocentric (Sun-centered) model of the solar system, spearheaded by Copernicus, involved a fundamental change in perspective. It required re-evaluating existing observations, identifying a more consistent and simpler underlying principle, and building a new framework to understand celestial mechanics. This highlights the importance of challenging existing paradigms and seeking more fundamental explanations.
  • The Theory of Evolution: From early ideas of species change to Darwin’s theory of natural selection, requiring the identification of core principles (variation, selection, inheritance), understanding their interactions, and building a framework to explain the diversity of life.

These examples, along with the Atomic Theory, showcase how significant advancements often involve:

  • Systematic Observation and Data Collection: Gathering information and identifying patterns.
  • Abstraction of Core Concepts: Identifying the fundamental elements or principles at play.
  • Formalization of Relationships: Defining how these concepts interact and relate to each other.
  • Building a Comprehensive Model: Creating a structured framework that explains existing observations and potentially predicts new phenomena.
  • Iterative Refinement: Continuously testing and improving the model based on new evidence and insights.

Best regards,
Ihor Ivliev

5

@dtonhofer

Regarding the question: “What would be the “core”?”

The foundational layer is called the Transparent Integral Core (TIC), whose essential part (the “core”) is known as the Thinking Tools (model/module, etc.).

So, what exactly are the Thinking Tools?

In my current vision/daydreaming/understanding, they can be described/characterized as follows (and you might want to check the alternative descriptions provided in the links below):

“Thinking tools” are envisioned as structured, modular, and configurable cognitive primitives (including methodologies, strategies, and other applicable human reasoning patterns) with formal verification methods. These serve as the foundational “source code” for building transparent and ethical reasoning systems, particularly in high-stakes AI architectures. They encompass various modes of thought, ranging from heuristic approaches to formal, mathematically grounded models, aiming to enhance reasoning and decision-making in a transparent and interpretable manner, both within computational models (like AI) and as standalone methods.

These tools could be positioned at the intersection of domains such as (but not limited to):

  • Cognitive Science: Mental models, dual-process theory, etc.
  • Formal Logic: Truth preservation systems, etc.
  • Decision Theory: Rational choice frameworks, utility maximization, etc.
  • AI Ethics: Explainability requirements/standards, auditing protocols/standards, etc.

Corpus Development:

Creating the initial tool catalog (to gradually develop a core set, extended catalog, and ultimately a full catalog) is estimated to require thousands of researcher-hours and/or the assistance of advanced AI. The process will involve:

  • Core Set: Expert-validated primitives.
  • Extended/Full Catalog: Collaboration of experts and AI-assisted discovery with human oversight, ideally transitioning to ML-driven proto-formal conversion in the future.

Validation Methods:

A mix of methods will be employed for cataloging and validating these tools:

  • Expert consensus
  • Crowdsourced validation
  • Advanced-AI automated validation (initially, or later as a partial component in a human-in-the-loop system).

Key Features of Thinking Tools (Iteratively Developed):

  • Modular and Configurable Nature: Designed as independent, understandable, and improvable components with standardized input/output interfaces, allowing for flexible combination and adaptation to various tasks, enabling dynamic and context-aware reasoning. This modularity and these standardized interfaces are specifically intended to facilitate the seamless integration of the latest, newly devised human (or AI-invented/designed) fully transparent and interpretable thinking, reasoning, and understanding methods, frameworks, and approaches, ensuring CCACS can continuously evolve and leverage cutting-edge insights from both human and artificial intelligence.

  • Variable Levels of Hybrid Formalization: Recognizing the complexity of cognition, these tools will range from fully formalized logical systems to proto-formalized and fluid heuristics, enabling nuanced representation of human reasoning. This hybrid approach combines mathematically rigorous methods with expert-validated proto-formal and fluid tools, mirroring the graded structure of human reasoning and supported by formalization adjusters from other layers.

  • Adaptive Rigor: Domain-specific formalization will be allowed, ranging from strict logic in areas like aviation safety to fluid heuristics in creative design. This will be supported by domain-specific validation:

    • Medical: FDA trial protocols.
    • Legal: Precedent alignment checks.

  • Systematic Organization: Organized and cataloged within a comprehensive “Thinking Tools Corpus/Glossary/Lexicon,” facilitating effective selection and application.

  • Emphasis on Transparency and Interpretability: Application is designed to ensure a visible and auditable reasoning process, fostering trust and accountability. This includes the generation of causal pathway visualizations and immutable reasoning traces (Blockchain-immutable reasoning traces with causal pathway visualizations and confidence decay tracking via Hyperledger-based audit logs).

  • Explicit Uncertainty Handling: The tools and their implementation will incorporate mechanisms to represent, manage, and communicate uncertainty (Multi-modal representation including probability bounds, fuzzy confidence scores, and epistemic gap markers, with an integrated uncertainty propagator in the Transparent Integral Core).

  • Interdisciplinary Foundation: Grounded in established research and principles from multiple scientific domains, ensuring a robust and comprehensive approach.

  • Ethical Auditability: Designed to provide court-admissible reasoning traces for consequential decisions, addressing the “black box dilemma” and enabling ethical oversight of complex AI systems.

Article 1 (medium, semi-wordy): Comprehensible Configurable Adaptive Cognitive Structure (CCACS)

Article 2 (big, wordy): Towards Transparent AI: Introducing CCACS – A Comprehensible Cognitive Architecture – Ihor Ivliev

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Here are the updated links with improved explanations and a more refined vision of the CCACS and ACCCU frameworks:

Shorter, more digestible version (high-level overview): How to build AI you can actually Trust - Like a Medical Team, Not a Black Box

Longer, detailed version (with diagrams, tables, architecture): Adaptive Composable Cognitive Core Unit (ACCCU) | by Ihor Ivliev | Mar, 2025 | Medium

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Hard - A Reference Architecture for Transparent and Ethically Governed AI in High-Stakes Domains - Generalized Comprehensible Configurable Adaptive Cognitive Structure (G-CCACS)

8

@dtonhofer

I hope you are doing well :slight_smile:

G-CCACS v.3:

  1. Criticism: Grand Vision vs. Realization & Practicality
  • Concern: The vision is grand, but how is it realized? Will it work beyond trivial examples or communicate effectively?
  • G-CCACS Address: The document presents G-CCACS as a detailed conceptual reference architecture , acknowledging it’s a blueprint requiring implementation and validation. It details specific layers, components (like NormKernel, TIC), metrics (CFS, SURD, FDR), and protocols (EEP, GCVP) outlining how the vision could be realized. Case studies in healthcare and law are included to illustrate applicability beyond trivial examples. Effective communication is addressed via the Cross-Modal Explanation Renderer. While practicality is still subject to implementation, the G-CCACS document provides a much more concrete realization plan than the criticism implies was present before.
  1. Criticism: Defining the “Transparent Core”
  • Concern: What is the “transparent core”? How does it ensure functionality?
  • G-CCACS Address: G-CCACS defines a Transparent Integral Core (TIC) layer. Its function is explicitly stated: to deterministically execute only the most rigorously validated (G1 grade) and formally verified rules received from the FORMALIZATION layer. This clearly defines the core and its mechanism for ensuring high-assurance functionality based on verified knowledge.
  1. Criticism: Explainability, Models, and “Just So” Stories
  • Concern: NNs lack whiteboard models needed for explanation. Human explanations are often flawed (“just so” stories). AI explanations might just be post-hoc arguments (Toulmin model), not the real reason.
  • G-CCACS Address: G-CCACS directly confronts this.
    • It aims for faithful explanations grounded in the system’s actual reasoning path, meticulously logged in EpistemicState objects (capturing causal and validation traces). This counters the “just so” story or post-hoc argument concern.
    • It incorporates interpretability tools (NeuroLens, CausalCircuitTracker) for opaque components and the ConceptGate to enforce reasoning through human-understandable concepts, addressing the lack of inherent models in NNs.
    • The Cross-Modal Explanation Renderer aims to provide understandable explanations, with quality assessed (e.g., μcm score).
  1. Criticism: Level of Assurance (Idealization vs. Reality)
  • Concern: Logic/math offer proofs (like Catala), but is that always needed? Depends on the desired level of assurance.
  • G-CCACS Address: G-CCACS embraces this nuance. It integrates formal methods (Z3, Lean4) in the FORMALIZATION layer specifically for high-assurance (G1) rules and critical components. However, it also includes the G4→G1 grading system, acknowledging that not all knowledge requires or can achieve proof-level certainty. This allows for different levels of assurance matched to the criticality and validation status of the knowledge.
  1. Criticism: Need for Commonsense Knowledge (Cyc Example)
  • Concern: True understanding and explanation likely require commonsense knowledge, citing Cyc’s role in bridging ML correlations with causal pathways.
  • G-CCACS Address: The G-CCACS concept explicitly incorporates a Common-Sense Grounding Protocol (CSGP) leveraging external knowledge bases (like ConceptNet, ATOMIC) within the CONTEXT layer. This mechanism is designed precisely to augment reasoning with commonsense knowledge, validate inferences, and potentially bridge gaps similar to the way described for Cyc in the Cleveland Clinic example.
  1. Criticism: Neural Networks lack a traditional, interpretable “whiteboard model”.
  • Concern: To explain something effectively, one typically needs a model (like equations or rules) that can be examined and reasoned about. Neural Networks, often called “models,” function more like complex machines (“algorithmic models” in Breiman’s terms) without this easily inspectable structure, making them inherently difficult to explain.
  • G-CCACS Address: G-CCACS conceptually addresses this challenge through several integrated mechanisms:
    • Layered Architecture & Hybrid Models: G-CCACS uses a multi-layered approach. While NNs might be used in lower layers (like PATTERN), higher layers (CONTEXT, FORMALIZATION, TIC) operate on more structured, “whiteboard-friendly” representations like causal graphs and formal logical rules, providing interpretable models for those reasoning stages.
    • ConceptGate as Interface: This mechanism acts as a “concept bottleneck,” forcing outputs from potentially opaque NN components to be mapped onto human-understandable concepts before being used by higher reasoning layers. This creates an interpretable intermediate layer or conceptual model, providing a basis for explanation even if the NN itself isn’t simple.
    • Process Traceability Focus: Instead of solely trying to explain the NN’s internal state, G-CCACS emphasizes explaining the traceable process of how information flows and is validated across layers. EpistemicState objects log this entire journey. The explanation is grounded in this verifiable operational history.
    • Mechanistic Interpretability Tools: G-CCACS includes tools like NeuroLens and CausalCircuitTracker specifically designed to probe and visualize the internal workings of NN components, attempting to build understanding of the “machine” model even without simple equations.