Sibyl is a highly-configurable system for developing usable ML applications for any domain. Sibyl creates explanations of ML models that are rapidly understandable to audiences of all kinds, including those without ML experience.

📚 Pyreal: A Python library for automatically generating explanations of ML models that are easy to read and use.

💻 Sibyl-API: A REST-API layer enabling a wide variety of interactions with ML models, from getting information, explaining, tuning, and annotating.

📈 Sibylapp2: A lightweight front-end application for interacting with and understandig ML models.

🦜 Explingo: Using LLMs to create natural language explanations of ML logic.

⚕️ VBridge: Interactive visualization for electronic health records.

Sibyl for Child Welfare Screening: Zytek, A., Liu, D., Vaithianathan, R., & Veeramachaneni, K. (2021). Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making. IEEE Transactions on Visualization and Computer Graphics. Link

Sibyl for Wind Turbine Monitoring: Zytek, A., Wang, W.-E., Koukoura, S., & Veeramachaneni, K. (2023, October 27). Lessons from Usable ML Deployments Applied to Wind Turbine Monitoring. Neurips 2023: XAI in Action: Past, Present, and Future Applications. Link

Pyreal: System for Interpretable Explanations: Zytek, A., Wang, W., Liu, D., Berti-Equille, L., & Veeramachaneni, K. (2023). Pyreal: A Framework for Interpretable ML Explanations. Link

Narrative Explanations using LLMs: Zytek, A., Pido, S., Veeramachaneni, K. (2024). LLMs for XAI: Future Directions for Explaining Explanations. In ACM CHI HCXAI. Link

VBridge for Electronic Health Records: Cheng, F., Liu, D., Du, F., Lin, Y., Zytek, A., Li, H., Qu, H., & Veeramachaneni, K. (2021). VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models. IEEE Transactions on Visualization and Computer Graphics. Link