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Externalizing Judgment in AI Systems — A Decision-Oriented Signal Platform Built on Ontology, DSL, and Behavior Trees

Many AI platforms are designed with a single dominant focus: How can we make the model smarter? Improve accuracy. ...

2026.02.22

ICT技術:ICT Technology人工知能:Artificial Intelligence

Designing Progressive Trust Scores as Contracts — Treating Trust Not as a Learned Outcome, but as a Revocable Boundary —

Trust scores are convenient.When there is a number, people feel reassured. But there is a fatal misunderstanding about ...

2026.02.21

ICT技術:ICT Technology人工知能:Artificial Intelligence

Externalizing Judgment in Machine Learning Systems — The Design Philosophy Behind the Decision-Oriented Signal Platform —

Most AI platforms are designed around a single goal: building smarter models. Improve accuracy.Increase inference speed...

2026.02.20

Uncategorized

Exceptions Are Not Failures — How to Integrate Exception Handling into Design, and Why More Exceptions Can Mean a Healthier System —

A system with many exceptions is often seen as“unfinished,”“unstable,”or “poorly designed.” That is why many AI and bus...

2026.02.19

ICT技術:ICT Technology人工知能:Artificial Intelligence

Who Owns the Objective Function? — The Implicit Judgments Embedded in Scores and the Problem of the Values Humans Did Not Write Down —

AI is often believed to be fair.It evaluates through numbers, remains unaffected by emotion, and produces consistent jud...

2026.02.18

ICT技術:ICT Technology人工知能:Artificial Intelligence

Why AI Cannot Possess “Common Sense” — Common Sense as a Collection of Discontinuities, and Common Sense as Ontology —

AI knows an enormous amount. Wider than an encyclopedia.Faster than a specialist. And yet, at a certain moment, we fee...

2026.02.17

ICT技術:ICT Technology人工知能:Artificial Intelligence

Why “Explainable AI” Does Not Constitute a Fundamental Explanation — The Difference Between Explanation and Justification, and the Division of Roles Between Logic and Accountability

There is a term called “Explainable AI (XAI).”AI that is not a black box.AI that can explain the grounds of its decision...

2026.02.16

ICT技術:ICT Technology人工知能:Artificial Intelligence

Probability Is Not for Reducing Uncertainty — The True Role of Probability and the Perspective of Designing to Preserve Ambiguity

When probabilities are presented, people feel reassured. Success probability: 82%Risk: 18%Confidence: 0.91 When number...

2026.02.15

ICT技術:ICT Technology人工知能:Artificial Intelligence

What Happens When Optimization Runs Wild — The Moment a Loss Function Distorts the World, and How to Design for Goodhart’s Law

AI Is Good at Optimization.In fact, that is all it can do. Precisely because of that,when optimization runs wild, the w...

2026.02.14

ICT技術:ICT Technology人工知能:Artificial Intelligence

Why AI Cannot Bear Responsibility — Why Judgment and Responsibility Cannot Be Separated, and Why Logic Must Be Externalized

AI produces answers with astonishing accuracy.Sometimes faster than humans, more comprehensive, more consistent, more ca...

2026.02.13

ICT技術:ICT Technology人工知能:Artificial Intelligence

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Externalizing Judgment in AI Systems — A Decision-Oriented Signal Platform Built on Ontology, DSL, and Behavior Trees

Designing Progressive Trust Scores as Contracts — Treating Trust Not as a Learned Outcome, but as a Revocable Boundary —

Externalizing Judgment in Machine Learning Systems — The Design Philosophy Behind the Decision-Oriented Signal Platform —

Exceptions Are Not Failures — How to Integrate Exception Handling into Design, and Why More Exceptions Can Mean a Healthier System —

Who Owns the Objective Function? — The Implicit Judgments Embedded in Scores and the Problem of the Values Humans Did Not Write Down —

Why AI Cannot Possess “Common Sense” — Common Sense as a Collection of Discontinuities, and Common Sense as Ontology —

Why “Explainable AI” Does Not Constitute a Fundamental Explanation — The Difference Between Explanation and Justification, and the Division of Roles Between Logic and Accountability

Probability Is Not for Reducing Uncertainty — The True Role of Probability and the Perspective of Designing to Preserve Ambiguity

What Happens When Optimization Runs Wild — The Moment a Loss Function Distorts the World, and How to Design for Goodhart’s Law

Why AI Cannot Bear Responsibility — Why Judgment and Responsibility Cannot Be Separated, and Why Logic Must Be Externalized

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