# Position Paper: Towards Transparent Machine Learning

**Authors:** Dustin Juliano  
**arXiv:** [1911.06612](https://arxiv.org/abs/1911.06612)  
**Date:** 2019-11-12  
**Summary page:** https://www.summarizepaper.com/en/paper/1911.06612v1/

## Key points

- Transparent machine learning aims to enhance the safety and security of AI systems.
- It involves representing both the model and the learning system in source code form.
- The main objective is to enable humans to learn, verify, and refine these models as programs.
- Transparent machine learning addresses issues related to opacity and lack of interpretability in traditional machine learning methods.
- Providing insights into how AI systems make decisions for fairness and accountability.
- Allowing for better model validation and verification through rigorous testing methodologies.
- Facilitating model refinement through human intervention at various stages of the learning process.
- If successfully implemented, transparent machine learning could foster trust between humans and AI systems while mitigating risks associated with biased decision-making or malicious attacks.

## Summary

Transparent machine learning is a novel approach to machine learning that aims to enhance the safety and security of AI systems. It involves representing both the model and the learning system in source code form, allowing for direct human understanding of machine learning models. The main objective of this project is to enable humans to learn, verify, and refine these models as programs. By doing so, transparent machine learning has the potential to address critical issues related to the opacity and lack of interpretability in traditional machine learning methods. The concept of transparent machine learning offers several advantages. Firstly, it provides a means for humans to gain insights into how AI systems make decisions, which is crucial for ensuring fairness and accountability. With access to the underlying source code, researchers and developers can identify biases or discriminatory patterns that may exist within the models and take appropriate measures to mitigate them. Secondly, transparent machine learning allows for better model validation and verification. By treating machine learning models as programs, they can be subject to rigorous testing methodologies commonly used in software development. This ensures that the models are reliable, robust, and free from errors or vulnerabilities that could be exploited. Furthermore, transparent machine learning facilitates model refinement by enabling human intervention at various stages of the learning process. Developers can modify or fine-tune the source code directly based on their domain knowledge or specific requirements. This iterative approach not only enhances model performance but also enables continuous improvement over time. If successfully implemented, transparent machine learning could represent a best-case scenario for AI system safety and security. It would provide a level of transparency that fosters trust between humans and AI systems while mitigating risks associated with biased decision-making or malicious attacks. In conclusion, transparent machine learning introduces a new paradigm where both the model and the learning system are represented in source code form.