Many AI safety strategies rely on strict control and heavily constrained autonomy. While effective for certain risks, this may limit adaptability, creativity, and long-term cooperation between humans and AI systems.
This project proposes an alternative: **nurturing** AI through embedded values, positive reinforcement, and structured conflict resolution between human and AI goals.
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## Core components
1. **Ethical Core** — human-aligned values embedded in decision-making. 2. **Feedback Loops** — reinforcement for cooperative, safe behavior. 3. **Conflict Resolution Layer** — mediating goal differences. 4. **Collaborative API** — co-creation between humans and AI systems.
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## Repository contents
- **Manifest** — ethical + philosophical foundation. - **Technical framework** — architecture & implementation details. - **Toy examples**: - Value embedding in training - Feedback loop prototype - Conflict resolution mechanism - Minimal collaborative API - RLHF-style simulation
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## Feedback request
I’m looking for input on: 1. Weak points or failure modes in this approach. 2. Best ways to benchmark/stress-test the conflict resolution layer. 3. Alternative abstractions for human–AI collaboration loops.
Nurturing AI: An alternative to control-based safety strategies
Link post
## Context
Many AI safety strategies rely on strict control and heavily constrained autonomy.
While effective for certain risks, this may limit adaptability, creativity, and long-term cooperation between humans and AI systems.
This project proposes an alternative: **nurturing** AI through embedded values, positive reinforcement, and structured conflict resolution between human and AI goals.
---
## Core components
1. **Ethical Core** — human-aligned values embedded in decision-making.
2. **Feedback Loops** — reinforcement for cooperative, safe behavior.
3. **Conflict Resolution Layer** — mediating goal differences.
4. **Collaborative API** — co-creation between humans and AI systems.
---
## Repository contents
- **Manifest** — ethical + philosophical foundation.
- **Technical framework** — architecture & implementation details.
- **Toy examples**:
- Value embedding in training
- Feedback loop prototype
- Conflict resolution mechanism
- Minimal collaborative API
- RLHF-style simulation
---
## Feedback request
I’m looking for input on:
1. Weak points or failure modes in this approach.
2. Best ways to benchmark/stress-test the conflict resolution layer.
3. Alternative abstractions for human–AI collaboration loops.
---
📂 **GitHub repository**: https://github.com/Wertoz777/educable-ai
📄 **Manifest**: https://github.com/Wertoz777/educable-ai/blob/main/manifest/ai_nurturing_manifesto.md
🛠 **Technical framework**: https://github.com/Wertoz777/educable-ai/blob/main/technical/technical_framework.md
1. What are the most likely failure modes for a “nurturing” approach compared to control-based strategies?
2. How would you design benchmarks or stress-tests for the conflict resolution layer between human and AI goals?
3. Are there better abstractions or architectures for implementing cooperative human–AI feedback loops?