With new Garage project Trove, people can contribute photos to help developers build AI models

Sep 23, 2020 Update Trove is now open to try on Android and a new web app! To learn more, read the full story on the Garage Blog.

Every day, developers and researchers are finding creative ways to leverage AI to augment human intelligence and solve tough problems. Whether they’re training a computer vision model that can spot endangered snow leopards or help us do our business expenses more easily when we scan pictures of receipts, they need a lot of quality pictures to do it. Developers usually crowd source these large batches of pictures by enlisting the help of gig workers to submit photos, but often, these calls for photos feel like a black box. Participants have little insight into why they’re submitting a photo and can feel like their time was lost when their submissions are rejected without explanation. At the same time, developers can find that these sourcing projects take a long time to complete due to lower quality and less diverse inputs.

We’re excited to announce that Trove, a Microsoft Garage project, is exploring a solution that can enhance the experience and agency for both parties. Trove is a marketplace app that allows people to contribute photos to AI projects that developers can then use to train machine learning models. Interested parties can request an invite to join the experiment as a contributor or developer. Trove is currently accepting a small number of participants in the United States on both Android and iOS.

A marketplace that puts transparency and choice first

Today, most data collection is passive, with many people unaware that their data is being collected or not making a real-time, active choice to contribute their information. And even those who contribute more directly to model training projects are often not provided the greater context and purpose of the project; there’s little to no feedback loop to correct and align data submissions to better fit the needs of project.

For people who rely on this data gig work as an important source of income, this rejection experience can leave them feeling frustrated and without any agency to contribute better submissions and a higher return on their time investment. With machine learning being a critical step in unlocking advancements from speech to image recognition, there’s an important opportunity to increase the quality of data, while making sure that contributors have the clarity and choice they need to participate in the process.

The Trove team has found a way to overcome these tough tradeoffs in a marketplace solution that emphasizes greater communication, context, and feedback between developers and project participants. “There’s a better way we can do this. You can have the transparency of how your data is being used and actually want to opt in to contribute to these projects and advance science and AI,” shares Krishnan Raghupathi, the Senior Program Manager for Trove. “We’d love to see this become a community where people are a key part of the project.”

To read more about key features and how Trove works for developers and contributors, check it out on the Garage Workbench.

Aspiring to higher quality data and increased contributor agency

The team behind Trove was originally inspired by thought leaders exploring how we can embrace the need for a large volume of data to enable AI advancements, while providing more agency to contributors and recognizing the value of their data. “We wanted to explore these concepts through something concrete,” shared Christian Liensberger, the lead Principal Program Manager on the project. “We decided to form an incubation team and build something that could show how things could be different.”

In creating Trove, the incubation team had to think through principles that would guide them as they brought such an experience to life. They believe that the best framework to produce the higher quality data needed to train these AI models involves connecting content creators to AI developers more directly. Trove was built with a design and approach that focuses on four core principles:

  • Transparency See all the projects available, details about who is posting them, and how your data will be used
  • Control Decide which projects you want to contribute to, and control when and how much you contribute
  • Enrichment Learn directly from AI developers how your contributions are valuable, and see how your participation will advance AI projects
  • Connection Communicate with AI developers to stay informed on projects you contributed to

“I love working on this project, it’s a continuous shift between the user need for privacy and control, and professionals’ need for data to innovate and create new products,” said Devis Lucato, Principal Engineering Manager for Trove. “We’re pushing the boundaries of all the technologies that we touch, exploring new features and challenging decisions determined by the status quo.”

Before releasing this experiment to external users, the team piloted Trove with Microsoft employees from across the US. While Trove is still in an experimental phase, the team is excited for even more feedback. “Our solution is still a bit rough around the edges, but we want to hear from the community about what we should focus on next,” shares Christian. Trinh Duong, the Marketing Manager on the project added, “My favorite part about working on this has been how much the app incorporates users into the experience. We want to invite our users to reach out and join us as true participants in the creation of this concept.”

The team is welcoming feedback from experiment participants here, and is enthusiastic for the input of users who are as passionate about the principles of transparency, control, enrichment, and connection as they are.

Request an invite and share your feedback

Trove will be able to try in the United States upon request while room in the experiment is still available. Request an invite to join the experiment, or request to add an ML project to the experiment.