02_ Reward Reports: Dynamic AI Documentation 

FALL / 2021
For A start-up branching off Cornel Tech’s Digital Life Initiative Lab
Role UX Researcher, FrontEnd Developer


Project Github ︎︎︎
  Most Recent Whitepaper ︎︎︎
 Check the current MVP ︎︎︎

           Building on the documentation frameworks for “model cards” and “datasheets” proposed by Mitchell et al. and Gebru et al., we argue the need for Reward Reports for AI systems. In a whitepaper recently published by the Center for Long-Term Cybersecurity, we introduced Reward Reports as living documents for proposed RL deployments that demarcate design choices. However, many questions remain about the applicability of this framework to different RL applications, roadblocks to system interpretability, and the resonances between deployed supervised machine learning systems and the sequential decision-making utilized in RL. At a minimum, Reward Reports are an opportunity for RL practitioners to deliberate on these questions and begin the work of deciding how to resolve them in practice.

As the UX Researcher and Frontend Dedeloper on the team, I had written an IRB, created the research instruments, and conducted focus group interviews with reinforcement learning researchers, industry leaders, policymakers, and users of RL based-tools. Finally, I conducted thematic analysis upon the interview transcripts.

Using the findings, I am currently refining the UX and feature deisgn, and implementing and building out a Reward Reports builder tool in collaboration with a backend developer in Node.