
REAL ML
REAL ML supports and advances interdisciplinary research that holds the use of algorithms and AI technologies to account—with a focus on the perspectives and experiences of marginalized communities
What if every AI analysis of public data came with a verifiable evidence record?
When you run an analysis of government open data, you can now publish it as a citable evidence package at a stable URL. Each package includes a full provenance chain including the exact prompt, which model ran, the data queries that were executed, and the source portal so anyone can trace how a conclusion was reached. The evidence page also includes a downloadable provenance graph in PROV-O, a W3C standard for representing how data was derived. This means the record isn't locked into one platform - it's structured, portable, and machine-readable. There's also an adversarial evaluation feature. Anyone can bring their own API key and have an independent LLM evaluate the analysis against a 6-criterion rubric covering data accuracy, bias detection, and methodological rigor. The evaluation gets attached to the evidence record as an attestation. One design choice I'm especially interested in feedback on: prompt visibility. You can publish the full text of your question (full transparency) or just a cryptographic hash (verifiable but private). A researcher replicating a study needs to see everything. A journalist investigating housing data might not want to reveal their angle. Same tool, both needs. This builds on the Civic AI Tools MCP server, which connects AI assistants to 559+ Socrata-based government open data portals (hoping to add CKAN and others soon!). The analysis layer was already working. This adds a trust layer on top of it so findings can be cited, challenged, and verified.


REAL ML supports and advances interdisciplinary research that holds the use of algorithms and AI technologies to account—with a focus on the perspectives and experiences of marginalized communities

The Taurus is a platform for creating and managing transparency notices for political advertisements as required by EU Regulation 2024/900.

Academic research project on algorithmic risk assessments mediating access to private renting, social housing, mortgages.
A 3-day interdisciplinary workshop bringing together researchers in algorithmic fairness.
The European Association for Algorithmic Fairness was founded in 2023 with the goal of fostering academic research and industrial implementation of algorithmic fairness of AI applications in the context of Europe and its legislative, societal, and cultural environment.