Data Labeling  for ML, part 5
We keep working on and publishing a range of articles related to data labeling for ML, gaining insights from the engineers and entrepreneurs behind the leading data labeling solutions, like Toloka, Snorkel, SuperAnnotate, LabelBox, and others.

We regularly check a few things with you via a simple survey. It takes about 3-5 minutes. Please respond to this survey – as a thank you, we will send you a cheat sheet with useful resources that help you understand and organize data labeling as well as a $50 promo code for your own project. Thank you!
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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. *
What do you think about the following company offering: "bespoke solutions for data labeling and machine learning"? What does the descriptor “bespoke” sound like to you? *
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6. 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
7. 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
8. Would your trust in data privacy/security of a company if the company changes its positioning? For example, if it went from an “engineer-to-engineer” approach (no frills, no fancy marketing packaging, very hands-on) to a solution for enterprise (fancy website, explaining how it’s an enterprise solution, lots of big tech clients featured)? *
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9. Would a controversy in the media make you change your trust in the data security & privacy of the data company involved? *
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10. Please give options of media controversies that would negatively affect your trust in the data security & privacy of the data company involved? *
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