Litterati

Christopher France

Christopher Blow France

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Exploratory design concepts: Can we imagine the Litterati data product as a game? I designed the Litterati "Data Editor" role as a supplement to the machine learning process that attempts to do better than simple Mechanical Turk style labeling factory. How can we treat data microtasking as an opportunity for civic engagement and genuine fun?
Exploratory design concepts: Can we imagine the Litterati data product as a game? I designed the Litterati "Data Editor" role as a supplement to the machine learning process that attempts to do better than simple Mechanical Turk style labeling factory. How can we treat data microtasking as an opportunity for civic engagement and genuine fun?

Litterati is a startup dedicated to community organizing for policy change. Litterati helps local teams of citizens organize to clean up beachfronts and urban areas. Users use an iOS or Android app to record images of the litter they collect; analysis of the litter data is used for strategic advocacy with governments and corporations. The Litterati app prominently uses machine learning to detect and automatically classify litter by brand, object type and material.

I have worked at Litterati as a data product strategist focused on optimizing data flow between team editors and a machine learning model. For my technical work with Litterati my tools are Node, Firebase a React frontend. I helped the team navigate a transition to open data licensing. I have also engaged at the executive strategic design process to support the development of positive feedback loops and network effects for growth.

Exploratory design concepts: What is the future of an operational dashboard for civic actors? This 2019 concept emphasizes spatial collections of activity.
Exploratory design concepts: What is the future of an operational dashboard for civic actors? This 2019 concept emphasizes spatial collections of activity.
Prototype: I designed a Litterati dashboard for users, a feature which visualizes personal and group activity.
Prototype: I designed a Litterati dashboard for users, a feature which visualizes personal and group activity.
Internal data product: I built a data table (React and Firebase) to manage the human curation of annotation data for the machine learning pipeline and reporting. The table is a collaborative interface with multiplayer mode for real-time data jam sessions. With this mechanism in place we have been able to align the Litterati litterat taxonomy with external categorization schemes (for example the U.N. litter taxonomy), strongly embedding Litterati in the ecosystem.
Internal data product: I built a data table (React and Firebase) to manage the human curation of annotation data for the machine learning pipeline and reporting. The table is a collaborative interface with multiplayer mode for real-time data jam sessions. With this mechanism in place we have been able to align the Litterati litterat taxonomy with external categorization schemes (for example the U.N. litter taxonomy), strongly embedding Litterati in the ecosystem.
Internal data product: I built a dashboard for internal stakeholders featuring detailed data visualizations with a light/dark theme toggle.
Internal data product: I built a dashboard for internal stakeholders featuring detailed data visualizations with a light/dark theme toggle.
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