# Responsible Use
irithyll is built for researchers, engineers, and builders working to understand and improve the world through streaming machine learning. This document outlines the principles behind the project and the uses we actively oppose.
## What irithyll is for
- **Scientific research** -- understanding data streams, concept drift, and online learning dynamics
- **Environmental monitoring** -- real-time sensor networks, climate modeling, biodiversity tracking
- **Healthcare** -- patient monitoring, anomaly detection in medical devices, streaming diagnostics
- **Education** -- learning how streaming ML works, from Hoeffding trees to spiking neural networks
- **Financial analysis** -- market microstructure research, risk modeling, fair pricing
- **Embedded intelligence** -- smart agriculture, industrial IoT, energy optimization
- **Open knowledge** -- every algorithm is based on published research, every reference is cited
## What we oppose
We built irithyll to advance human understanding, not to cause harm. We explicitly oppose the use of this software for:
- **Autonomous weapons systems** -- no machine should make kill decisions
- **Mass surveillance** -- tracking populations, social scoring, predictive policing
- **Discrimination** -- profiling people by race, religion, gender, or ethnicity
- **Environmental destruction** -- optimizing extraction or exploitation of natural systems
- **Suppression of rights** -- censorship, protest suppression, whistleblower identification
## Our position
irithyll is released under MIT and Apache-2.0 licenses. We cannot legally prevent misuse, and we are honest about that. What we can do is state our values clearly, build a community that shares them, and make this project a space where ethical engineering is the default.
If you are using irithyll for research, education, healthcare, environmental science, or any work that makes the world more understandable and more fair -- you are exactly who this project is for.
If you are building surveillance infrastructure, weapons guidance, or systems designed to control and harm people -- we did not build this for you, and we ask you to reconsider.
## For researchers
If you use irithyll in published work, please cite it:
```bibtex
@software{irithyll,
author = {Shawe, Jonathan Bailey},
title = {irithyll: Streaming Machine Learning in Rust},
url = {https://github.com/evilrat420/irithyll},
year = {2026}
}
```
We are particularly interested in hearing from researchers working on:
- Streaming ML for environmental monitoring and climate science
- Online learning in resource-constrained medical devices
- Concept drift in non-stationary real-world systems
- Novel applications of reservoir computing, spiking networks, or KAN architectures
- Fairness and interpretability in streaming settings
Open an issue or reach out -- we want to support your work.