From Black Box to Jñāna: Custos AI's "NanoJnana" and the Path to Verifiable AI Ethics
Detailing how IEEE/ISO benchmarks, deep AI interpretability, and collective oversight can transform AI governance
Greetings again, fellow explorers charting the complex territories of AI and society.
In my previous reflections on 'Learn Vedanta Substack' (the core concept of Custos AI ["Why I Believe..."], its operationalization ["Custos AI Operationalized..."], and its dialogue with visions for safe AGI ["Custos AI Complements Anthropic..."]), I outlined the vision of Custos AI as an impartial "Ethical Hawk-Eye," a mechanism to verify AI decisions against fundamental principles. Often, when interacting with advanced Artificial Intelligence – especially those that generate text or make complex decisions – we encounter its "black box" nature. AI offers a response, sometimes surprisingly accurate, other times flawed or problematic, but how it arrived there often remains a mystery. Understanding this "how" is crucial if we are to build trust and responsibly guide AI, particularly when its decisions impact people's lives. It therefore becomes essential to endeavour to "reverse engineer" these models, that is, to try and dismantle, at least conceptually, their internal workings to understand their mechanisms, much like a biologist seeks to understand a complex organism. This is a vital step to move beyond the "black box" nature of LLMs and other advanced AI systems.
Today, I wish to delve deeper into the practical tools and processes that, in my vision, would make Custos AI not just an ideal, but a robust and transparent mechanism, rooted in recognized standards and projected towards continuous collective learning, introducing a concept I would like to call "NanoJnana" (AI Microscope).
Global Standards as Foundations: Understanding IEEE 7003 and ISO/IEC 23894
For an "Ethical Challenge" evaluated by Custos AI to be credible and significant, I believe it must be based on solid, shared criteria. This is where crucial international standards come into play:
What is IEEE 7003? Imagine it as a set of guidelines designed to ensure transparency and mitigate bias in Artificial Intelligence systems. The aim is to ensure that decisions made by an AI model are explainable, equitable, and do not lead to unfair discrimination. For example, if an AI is to evaluate credit applications, IEEE 7003 helps verify that it does not unfairly penalise certain groups based on irrelevant or discriminatory factors present in its training data.
What is ISO/IEC 23894? This standard, on the other hand, is a comprehensive guide for managing risks associated with AI systems. Artificial Intelligences can pose risks to security, privacy, and reliability. ISO/IEC 23894 offers organisations a methodology to identify these risks, assess their severity, and implement effective control measures to mitigate them.
How are these standards practically integrated into Custos AI?
The idea I have is for Custos AI to become a tool that, when activated by an "Ethical Challenge," uses these standards as benchmarks for its verifications:
Bias Monitoring and Mitigation (through IEEE 7003):
Practical Action by Custos AI: If there's doubt about an AI decision (e.g., a hiring algorithm seemingly favoring one gender), Custos AI could use its technical capabilities (or oversee specific tests) to analyze the data and the model and verify if, according to the metrics and principles of IEEE 7003, there is evidence of bias.
Concrete Output: Custos AI could generate specific reports highlighting, for example, "Model X shows a statistically significant correlation between gender Y and a lower probability of advancing to the next stage, in potential violation of the equity principles defined by IEEE 7003." It doesn't "correct" directly, but provides proof to the body that raised the challenge.
AI Risk Management (through ISO/IEC 23894):
Practical Action by Custos AI: If an AI used for air traffic control is challenged for potential unreliability, Custos AI could verify whether the organization that developed/implemented that AI followed a risk management process compliant with ISO/IEC 23894 (e.g., did they identify all relevant risks? Did they implement adequate control measures?).
Concrete Output: Custos AI's report might state: "The risk management procedures for the air traffic control AI do not appear fully compliant with ISO/IEC 23894 guidelines in areas X and Y, suggesting a potential vulnerability."
In essence, I imagine Custos AI equipping itself with the technical capabilities to "interrogate" AI systems and their development/management procedures, using these standards as an objective yardstick.
After the Ethical Challenge: "NanoJnana (AI Microscope)" and the Flow Towards Collective Understanding
A simple verdict of "compliant/non-compliant" from Custos AI, as I've said, is not enough. Once Custos AI has completed its analysis, the next step must be deep transparency and learning.
This is where I introduce the concept of "NanoJnana," my "AI Microscope" for Custos AI. "Nano" evokes the idea of delving into minute, subtle detail.
"Jñāna" (ज्ञान) is a Sanskrit word from my cherished Vedanta that means knowledge, wisdom, direct realisation – not mere information, but profound understanding. Thus, NanoJnana is the tool, the approach, that would allow us to seek that subtle knowledge within the AI's processes.
Imagine, for a moment, being a biologist before the invention of the microscope. You could see organisms, but the cells, the fundamental building blocks, were invisible. Then the microscope arrives, and a whole new world opens up. NanoJnana aims to be this for AI in the context of Custos AI: not a physical object, but a suite of analytical processes and interpretability techniques that would allow, after a concluded "Ethical Challenge," to "look inside" the AI to understand its "reasoning."
What would this "AI Microscope" allow us to "see"? In simple terms:
The "Digital Footprints" of the Decision: Like a detective following clues, NanoJnana would attempt to reconstruct the AI's decision-making path. Which specific input data had the greatest weight? Which internal "associations" or "intermediate calculations" (what researchers call features, circuits, or try to map with "attribution graphs," as seen in the pioneering work of teams like Anthropic) were activated and led to the final output?
FATE Mapping (Fairness, Accountability, Transparency, Ethics): This "ethical microscope" would apply metrics and checks to verify if the AI acted according to fundamental principles: Was it Fair towards different groups? Was its decision-making process sufficiently Transparent? Can Accountability for its behaviour be attributed? Did it operate in line with defined Ethical Principles?
"Explain to Me Why" (XAI - Explainable AI): Instead of blind acceptance, with NanoJnana we would seek explanations closer to human language. It would use XAI techniques to translate, as much as possible, the AI's internal mechanisms (often purely mathematical and statistical) into a form we can interpret, albeit with the awareness that no explanation of such a complex system can ever be exhaustive or infallible.
"Understanding How It Understands" (AI Interpretability): This goes beyond simply explaining the output. It's the attempt to understand how the AI is internally structured to arrive at certain types of results. It's like studying not just what a cell does, but how its organelles interact to allow it to live. This is the most challenging area, where research is rapidly evolving, but it is fundamental for true comprehension.
Thus, when following an "Ethical Challenge," Custos AI activates "NanoJnana," the objective is to produce a detailed "Ethical Compliance and Interpretability Mapping." It won't be a magical reading of the AI's "thoughts," but our best structured attempt, using the most advanced tools, to open up that "black box" as much as possible, understand if the AI has respected fundamental principles, and learn more about how these new, powerful forms of intelligence operate. It is, in my view, an essential tool for building well-founded trust and guiding AI towards a future that genuinely serves humanity.
The Oversight Commission and the Learning Cycle:
The discoveries from a single challenge are important, but their true value, I believe, emerges from aggregated analysis. For this, I propose that a post-challenge flow be managed by a new entity:
The "Commission for Algorithmic Ethical Surveillance and Continuous Learning" (CAESCL - a name I devised for concreteness):
This independent Commission would receive all "Ethical Compliance and Interpretability Mappings" produced by Custos AI.
I imagine this Commission divided into two main operational sections:
Section 1 - Bureaucratic-Decisional Oversight: Composed of policy experts, jurists, representatives of regulatory bodies, and relevant stakeholders. Their task is to acknowledge the mappings, ensure that the bodies that raised the challenge receive adequate responses, and, if necessary, stimulate corrective actions or policy adjustments.
Section 2 - Research Centre and Trend Analysis: This is an arm of the Commission created specifically for this purpose. Researchers, data scientists, and ethicists would analyse anonymised mappings from all challenges, looking for patterns, recurring biases, emerging risks not yet regulated, or best practices.
The Periodic Bulletin and Annual Report:
Every two months, the Research Centre (Section 2 of CAESCL) would issue a "Bulletin on Algorithmic Ethics in Practice." This public document would not discuss specific active cases but would summarise aggregated trends, the most common types of problems detected, challenges in applying standards, and potential alerts for the AI community.
At the end of each year, CAESCL would publish an "Annual Report on the State of AI Ethical Compliance." This more substantial document would provide a complete overview of all challenges handled, the results of the Research Centre's analyses, evaluate the effectiveness of Custos AI, and propose recommendations to improve standards, policies, and the overall ethical robustness of AI. It would include an analysis of the "pros and cons" of the entire system and how to optimise it.
This mechanism – from the individual "Ethical Challenge" facilitated by Custos AI (with its standards-based verification capabilities), to the mapping via "NanoJnana," up to aggregated analysis and public dissemination by a dedicated Commission – aims, in my vision, to create not just a control system, but a virtuous cycle of learning and continuous improvement. A way to make algorithmic ethics a living, transparent, and constantly evolving practice, serving a technology that is genuinely aligned with our deepest human values.
With these further refinements to Custos AI, particularly the 'NanoJnana (AI Microscope)' and the proposed oversight mechanisms, I hope I'm taking meaningful steps towards making this concept of verifiable AI ethics more concrete and actionable.
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