> ML_LITERATURE // SAMBASIVAN-2021-EVERYONE-WANTS-TO-DO-THE-MODEL-WORK_v1.0
"Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI
Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, Lora M. Aroyo · ACM Conference on Human Factors in Computing Systems (CHI) (2021)
mlops-production2021industry-standardnotAssessed
Principal Contribution
Documented "data cascades" in high-stakes AI: compound, compounding negative events caused by poor data practices that degrade real-world production performance.
Operational Relevance
Serves as qualified reference for implementing task-data-validation in production systems.
Assumptions
- Underlying computational topology and mathematical bounds adhere to established convexity/smoothness guarantees
Limitations
- Hardware runtime speedups, privacy budgets, and convergence depend on hyperparameters and network communication limits
Connected Algorithms, Architectures & Tools
Related Algorithms:
Related Architectures:
Implementing Libraries: