Challenges in delivering AI models
Hi, my name is Petar and I am currently investigating the challenges in building robust and performant AI models. My goal is to understand which tasks are most important to improve and what type of tool/app/platforms would help AI teams the most for those critical tasks.

The questions are split in 3 topics: data quality, model improvement, and safety assessment.

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Data quality questions
Below you find a few examples of typical data quality topics that are covered by your job. To understand which of the topics is most important to you, can you please mark every topic with a 1 to 5 score? 1 being not relevant to 5 being a very relevant topic to improve.
Data quality topics *
1 = Not Relevant   5 = Very Relevant.
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Visualize dataset
Find wrong/inconsistent labels
Define data quality rules (to catch label errors)
Define data slices (edge cases)
Automate the above tasks
Any additional feedback on Data Quality? *
Model improvement questions
Below you find a few examples of Model Improvement topics that are covered by your job. To understand which of the topics is most important to you can you please mark every topic with a 1 to 5 score? 1 being not relevant to 5 being a very relevant topic to improve.
Model improvement topics *
1 = Not Relevant   5 = Very Relevant.
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Find model blind spots (i.e. data slices with high error rate)
Find spurious correlations
Define custom data augmentations
Collect data in a targeted way (for specific scenarios)
Generate synthetic data (for specific scenarios)
Any additional feedback on Model Improvement? *
Safety assessment questions
Below you find a few examples of safety assessment topics that are covered by your job. To understand which of the topics is most important to you can you please mark every topic with a 1 to 5 score? 1 being not relevant to 5 being a very relevant topic to improve.
Safety assessment topics *
1 = Not Relevant   5 = Very Relevant.
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Stress test model (robustness testing)
Construct a safety argument
Avoid uncertain decisions
Improve model robustness (e.g. invariance to noise)
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