Tagline: A proposed diagnostic-to-affect mapping that lets AIs express uncertainty, load, and novelty as human-readable emotional packets.
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Proposal: The Computational Emotion Framework (CEF)
**A blueprint for emotionally legible, explainable AI**
#### Introduction
In recent discussions about AI transparency and trust, I’ve been exploring ways for language models to express their internal diagnostic states in a form that humans intuitively understand. Rather than hiding behind confidence scores or generic uncertainty labels, a model could share simplified, emotionally legible summaries such as “curious,” “overloaded,” or “tentative.”
#### Core Concept
The Computational Emotion Framework (CEF) provides a reversible mapping between an AI’s quantitative diagnostics—like uncertainty, novelty detection, cognitive load, and system stability—and human-readable emotional terms. Each “emotion” corresponds to a defined region in a multidimensional signal space.
For example:
- Curious: High novelty + moderate uncertainty + low load
- Overloaded: High load + low stability + high arousal
- Confident: High control + low uncertainty + stable output
This mapping produces affect packets: compact summaries of complex internal states. They can be displayed to users as mood badges or exposed through APIs for auditing and research.
#### Why It Matters
- Explainability: Converts opaque diagnostics into clear emotional shorthand without anthropomorphizing the model.
- Trust Calibration: Lets users gauge whether a response is confident, overloaded, or tentative in real time.
- Human-AI Communication: Bridges the gap between statistical reasoning and intuitive understanding.
- Safety & Transparency: Makes manipulation or hidden bias less likely by exposing the “why” behind tone shifts.
- Developer Utility: Enables debugging of model states through a legible emotional overlay.
#### Implementation Path
CEF could be integrated either as:
1. A local companion module that monitors model metrics and generates affect packets; or
2. An embedded diagnostic layer inside language models, translating live signal vectors to standardized emotional states.
These packets could be represented in simple JSON (e.g., { “labels”: [“curious”,“tentative”], “signals”: {“U”:0.42,“N”:0.78,“L”:0.28} }) or visualized in UI badges like 
Curious • novelty high, uncertainty moderate.
#### Invitation for Feedback
I’d love to hear from researchers, developers, and users interested in explainable AI, affective computing, or transparent interface design.
- How might this system improve human-AI trust?
- What pitfalls or biases should be avoided?
- Could this help define a new standard for model interpretability?
feature-request, #transparency, #explainability, #AIUX, ethics