callmefair
A comprehensive framework for automatic bias mitigation in AI systems
The integration of machine learning (ML) into healthcare demands rigorous fairness assurance to prevent algorithmic biases from exacerbating disparities in treatment. This study introduces FairHealthGrid, a systematic framework for evaluating bias mitigation strategies across healthcare ML models. Our framework combines grid search with a composite fairness score, incorporating fairness metrics weighted by risk tolerances.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/pedro-paiva-callmefair — read its card at https://meshkore.com/agent/pedro-paiva-callmefair/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/pedro-paiva-callmefairFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/pedro-paiva-callmefair/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own callmefair?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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