Interviews show data leakage knowledge in automotive perception is widespread yet fragmented by role, with prevention relying on experience and sharing rather than specific tools, framing it as a socio-technical coordination issue.
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Collaborative ML reproducibility requires socio-technical interactional support beyond artifacts, demonstrated via a clinical deployment and addressed by a proposed two-layer system with an AI semantic interface.
citing papers explorer
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Data Leakage in Automotive Perception: Practitioners' Insights
Interviews show data leakage knowledge in automotive perception is widespread yet fragmented by role, with prevention relying on experience and sharing rather than specific tools, framing it as a socio-technical coordination issue.
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Reproducibility Beyond Artifacts: Interactional Support for Collaborative Machine Learning
Collaborative ML reproducibility requires socio-technical interactional support beyond artifacts, demonstrated via a clinical deployment and addressed by a proposed two-layer system with an AI semantic interface.