Data Labeling  for ML, part 4
We keep  a series of articles on data labeling for ML, gaining insight from the engineers and entrepreneurs behind the leading data labeling solutions, Toloka, Snorkel, SuperAnnotate, LabelBox, and more.

We would like to check a few things with you via a simple survey. It will take about 5 minutes.

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1. You are: *
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2. Industry you work in: *
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3. Which data labeling solutions have you ever heard of? *
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4. Have you discovered any new data labeling solutions during the past 6 months? If yes, please list them. *
5. Please name one factor that you consider the most important when choosing a data labeling solution (for example: quality control, data privacy & security, time-effectiveness, cost-effectiveness, scalability, integration into automated pipelines). *
6. What quality(ies) makes you love a data labeling tool? (Might be interface design, quality control features, community around the product — anything that would make you reluctant to look for alternatives). *
7. Would you consider the following solutions responsible/ethical? *
Yes
No
Haven’t had a chance to explore this solution
Toloka
Appen
CloudFactory
Scale AI
Amazon MTurk
SuperAnnotate
Playment
Snorkel
Sama
Labelbox
Kili
8. Please indicate all the statements that apply to you in regards to the Responsible AI movement *
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9. Please describe what you would consider “actionable” in Responsible AI? It might already exist or might be your idea. *
10. Would you trust the following solutions with data privacy & security? *
Yes
No
Haven’t had a chance to explore this solution
Toloka
Appen
CloudFactory
Scale AI
Amazon MTurk
SuperAnnotate
Playment
Snorkel
Sama
Labelbox
Kili
11. Which privacy and security functionality/certifications of a data labeling solution are required in your company? *
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