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Found 5 papers, 1 papers with code
Beyond Testers' Biases: Guiding Model Testing with Knowledge Bases using LLMs
Current model testing work has mostly focused on creating test cases.
MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities
Many organizations seek to ensure that machine learning (ML) and artificial intelligence (AI) systems work as intended in production but currently do not have a cohesive methodology in place to do so.
Capabilities for Better ML Engineering
In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences.
Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systems
However, end-to-end development of ML-enabled systems, as well as their seamless deployment and operations, remain a challenge.
Component Mismatches Are a Critical Bottleneck to Fielding AI-Enabled Systems in the Public Sector
As a result, assumptions and even descriptive language used by practitioners from these different disciplines can exacerbate other challenges to integrating ML/AI components into larger systems.
