Feedback on PAI’s Guidelines and Resources to Improve Conditions for Data Enrichment Workers

Partnership on AI (PAI) is committed to making a positive difference in the AI field. We recently released five worker-centric guidelines that AI practitioners should follow when designing a project involving enriched data and a library of resources to help practitioners meet these guidelines.

In brief, the Data Enrichment Sourcing Guidelines are:

Ensure all data enrichment workers are paid above the local living wage
Design and run a pilot before launching a data enrichment project
Identify appropriate data enrichment workers for the desired task
Provide verified instructions and/or training materials for data enrichment workers
Establish clear and regular communication mechanisms

Visit PAI’s data enrichment sourcing library to see the full set of resources we developed to help AI organizations formalize their data enrichment practices and have a positive impact on the lives of data enrichment workers.

To ensure that we are building an actionable path for AI practitioners to improve conditions for workers and that any developed guidance truly results in better conditions for data enrichment workers, we will continue to build on these resources. We plan to incorporate feedback from AI practitioners on usability, workers on their resulting experience, and the broader AI community on how to further raise standards for workers.

If you know someone who we should hear from, please share this form with them.

First Name	Last Name
Email	Title
Organization	We would only use this information to follow up with you on your feedback.

Which of these best describes you?

How would you describe your role/perspective?

Do you think these guidelines and resources are something your organization could put into use?

YesNoUnsure

Why do you think these guidelines and resources could or could not be used?

Which aspects of these guidelines do you already follow? (For review, see the guidelines above or download the full Data Enrichment Sourcing Guidelines here)

Which aspects of these guidelines do you not currently follow?

What do you think would make it easier for your organization to put these guidelines into practice? (Other than the resources in the resource library.)

Do you have feedback on any of the resources we shared here?

These guidelines were created to address some of the most prevalent concerns shared by workers and documented by researchers that enriched data-procuring organizations can have an impact on.

Namely, we tried to address the following:

wage uncertainty due to unclear rejection standards or instructions
lack of power to contest conditions and decisions
below living wages
unrealistic productivity targets
wasted time and rejected work due to misalignment between workers and tasks or due to unclear instructions/training materials
not being able to complete tasks or resolve questions due to inability to communicate with requesters

Do you think if an AI organization follows these guidelines, it would adequately address these challenges? (For review, see the guidelines above or download the full Data Enrichment Sourcing Guidelines here.) If so, please elaborate on why you think it would help. If not, please share ideas on how we can improve them.

Though we know this is not a comprehensive list of all of the challenges faced by data enrichment workers, can you share what issues we have missed which we should develop additional guidance around?

Can these resources help inform regulation of data enrichment or digital labor?

What type of information is missing to introduce regulation into how data enrichment workers should be treated by AI-developing organizations and vendors providing data enrichment services?

Please share any other thoughts, comments, or feedback you have on the resources, guidelines, and case study.

Is it okay if we contact you to follow up on your feedback?

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To build on this work, we will be putting together a working group to work through challenges across the data supply chain and its impact on workers.

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