A fully local voice agent that audits its own fairness at runtime — no cloud LLM ever touches your words.
Offline fairness metrics mean little if the deployed system drifts. ARIA scores every LLM response on five axes before surfacing it — calibration, faithfulness, consistency, equity, attribution — so fairness becomes a runtime property, not a one-time benchmark.
Speech flows Whisper → Qwen3-8B → 5-axis audit → Piper TTS, entirely on a single consumer GPU. 16 tools give the agent real capabilities — search, files, system control — while peak usage stays at 7.1 GB VRAM and 0 cloud calls leave the machine.
ARIA is the working proof that the fairness research is deployable: the same audit axes I study on paper run inline, in real time, on local hardware — privacy and accountability without a datacenter.
Your voice leaves the device, is processed on someone else's server, and the response is never checked for bias.
Every word is processed on-device, and every response is audited on five fairness axes before you hear it.
The tradeoff most local assistants skip: privacy and accountability, together