Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/wondermongering/cultural-neural-hermeneutics

Interdisciplinary research project exploring AI bias, interpretability, and cultural influence through computational models trained on diverse philosophical corpora. Python, PyTorch, Transformers, UMAP, Streamlit.
https://github.com/wondermongering/cultural-neural-hermeneutics

ai-ethics bias-detection computational-humanities cultural-bias cultural-studies epistemology explainable-ai hermeneutics interdisciplinary-project interpretability machine-learning neural-network topological-data-analysis

Last synced: 3 months ago
JSON representation

Interdisciplinary research project exploring AI bias, interpretability, and cultural influence through computational models trained on diverse philosophical corpora. Python, PyTorch, Transformers, UMAP, Streamlit.

Awesome Lists containing this project

README 

          
# Cultural Neural Hermeneutics

_Unveiling the Philosophical Underpinnings of AI_

Interdisciplinary research project exploring AI bias, interpretability, and cultural influence through computational models trained on diverse philosophical corpora. Python, PyTorch, Transformers, UMAP, Streamlit.



[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)



[![Project Status: Concept](https://img.shields.io/badge/Project%20Status-Concept-red.svg)](https://github.com/your-username/cultural-neural-hermeneutics)



**A novel interdisciplinary research project exploring the intersection of AI, philosophy, and cultural studies.**



📜 Elevator Pitch

We're training AI models on diverse philosophical traditions (Western, Eastern, and digital) and visualizing their learning processes to uncover hidden cultural biases embedded in AI systems. Think of it as computational archaeology for the future of thought—using code instead of shovels to excavate the foundational assumptions shaping artificial minds.

🔍 Project Overview

Cultural Neural Hermeneutics (CNH) is an interdisciplinary research initiative exploring how cultural and philosophical assumptions shape AI development and behavior. Our approach:



Train specialized AI models on distinct philosophical corpora from Western philosophy (e.g., Plato), Eastern philosophy (e.g., Laozi), and modern digital discourse.

Capture developmental trajectories through detailed snapshots of internal model states during training, including weights, activations, gradients, and outputs.

Apply advanced topological data analysis and dimensionality reduction techniques (UMAP, t-SNE) to visualize the evolution of each model's "conceptual space" over time.

Develop an interactive visualization platform allowing users to explore model developmental timelines, perturb internal representations, and observe semantic shifts.

Design AI architectures embodying different philosophical epistemologies (Future phase) comparing models inspired by Platonic, Pragmatist, Buddhist, and Cartesian frameworks.

Create self-reflective AI systems (Future phase) capable of analyzing and describing their own conceptual organization.



🌟 Why This Matters



AI Ethics & Bias Mitigation: Reveal cultural biases embedded within AI models to develop fairer, more equitable systems

Interpretability & Transparency: Create new methods for understanding AI reasoning processes, enhancing trust and accountability

Cross-Cultural Understanding: Gain insights into diverse philosophical perspectives through computational modeling

Novel AI Design Paradigms: Explore building AI systems inspired by non-Western philosophical traditions

Computational Humanities: Contribute empirical data to fields traditionally outside computational analysis



🗺️ Project Roadmap

Phase 1: The Developmental Anthropological Oracle (Current Focus)

Objective: Build an interactive system for visualizing and analyzing the neural developmental histories of AI models trained on different cultural corpora.

Status: Initial development

Key Tasks:



- Project conceptualization and planning

- Model Architecture: Finalize architecture selection (DistilBERT)

- Corpora Curation:



  - Western: Plato's Republic

  - Eastern: Tao Te Ching (Laozi)

  - Modern Digital: Curated, anonymized Reddit discussions



- Developmental Trajectory Capture:



  - Training/validation loss recording

  - Attention weight storage

  - Representational Similarity Analysis (RSA)

  - Model weight, activation, and gradient preservation



- Topological Mapping: Implement UMAP and t-SNE visualizations

- Interactive Interface (Streamlit):



  - Timeline slider implementation

  - Concept input functionality

  - Neuron perturbation tools

  - Cross-corpora comparison views



- Initial Experiments: Run pilot studies and analyze results

- Documentation: Prepare code release and documentation



Phase 2: The Epistemological Ensemble Experiment

Objective: Implement and empirically test neural architectures embodying distinct philosophical epistemologies.

Status: Planned

Key Tasks:



- Design philosophical architectures (Platonic, Pragmatist, Buddhist, Cartesian)

- Define standardized comparative evaluation tasks

- Conduct comparative experiments and analysis



Phase 3: The Hermeneutic Loop: AI Self-Interpretation

Objective: Develop a meta-learning system capable of analyzing its own conceptual organization.

Status: Future Work

Key Tasks:



- Develop self-interpretive framework

- Implement recursive interpretability training

- Build conversational interface for model querying



🛠️ Technology Stack



- Languages: Python

- ML Frameworks: PyTorch, Transformers (Hugging Face)

- Dimensionality Reduction: UMAP, t-SNE

- Topological Analysis: GUDHI, Ripser

- Interpretability Tools: SHAP, Ecco

- Visualization: Streamlit, D3.js

- Version Control: Git, GitHub



🤝 Getting Involved

We welcome contributions from researchers, developers, and anyone interested in this interdisciplinary frontier! We're actively seeking collaborators with expertise in:



- Machine Learning & NLP

- Philosophy (Western and Eastern traditions)

- Data Visualization

- Project Management



How to Contribute:



- Read this README thoroughly

- Check the "Issues" tab for open tasks

- Fork this repository

- Create a branch for your contribution

- Submit a pull request



We're committed to fostering an inclusive community. Please adhere to our Code of Conduct.

📚 Intellectual Property and Open Source

This project balances intellectual property considerations with open-source principles:



- Code and tools released under MIT license

- Novel methodologies may be filed as provisional patents

- Research findings published in peer-reviewed venues



This is a living document that will evolve with the project. We encourage you to watch this repository for updates and join us in this exciting interdisciplinary exploration at the intersection of AI, philosophy, and cultural studies.