From 6f8df7253f95cae72c8c293d34fcefc7a6971744 Mon Sep 17 00:00:00 2001 From: Bipin Rimal <146849810+BipinRimal314@users.noreply.github.com> Date: Sun, 22 Mar 2026 23:51:06 +0545 Subject: [PATCH] fix: Article 50 end-user scope + Article 13 logs vs documentation distinction --- docs/my-website/docs/eu-ai-act-compliance.md | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/docs/my-website/docs/eu-ai-act-compliance.md b/docs/my-website/docs/eu-ai-act-compliance.md index fe852545ef5..d932f214ac7 100644 --- a/docs/my-website/docs/eu-ai-act-compliance.md +++ b/docs/my-website/docs/eu-ai-act-compliance.md @@ -122,11 +122,15 @@ Connect to a persistent backend (Langfuse, Helicone, or your own database). Set Article 13 requires providers of high-risk AI systems to supply deployers with sufficient information — instructions for use, accuracy metrics, known limitations — to operate the system appropriately. This is provider-to-deployer transparency. -LiteLLM's contribution to Article 13: -- **Model routing is logged** — deployers can see which model handled each request -- **Cost attribution** — deployers know which features consume the most AI resources -- **Fallback chains are visible** — when a primary model fails and a fallback serves the response, this is logged -- **Provider documentation pass-through** — LiteLLM's docs link to each provider's model cards and usage policies +Article 13 compliance requires the provider of the high-risk system to produce and maintain system-level documentation: intended purpose, accuracy and robustness metrics, known risks, and technical measures for monitoring. This is a documentation obligation, not a logging obligation. + +LiteLLM's observability features provide **supporting evidence** that can inform Article 13 documentation, but they do not satisfy Article 13 on their own: +- **Model routing logs** — help compile which models are in use and how requests are distributed +- **Cost attribution** — supports resource usage documentation +- **Fallback chain visibility** — provides evidence of system behavior under failure conditions +- **Provider documentation links** — LiteLLM links to upstream model cards, but these describe the LLM providers' models, not your high-risk AI system as a whole + +You must independently produce system documentation that covers how your specific deployment uses LiteLLM, its intended purpose, performance characteristics, and residual risks. ## Article 50: End-user transparency @@ -134,8 +138,7 @@ Article 50 requires deployers to inform end users that they are interacting with What you need to add: - User-facing disclosure that AI is involved in generating responses -- Documentation of which models are active and their known limitations -- Information about how routing decisions are made (cost, latency, quality) +- A mechanism for users to identify when an AI-generated response has been delivered (e.g., clear labeling in the UI) Note: Article 50 applies to chatbots and systems interacting directly with natural persons. It has a separate scope from the "high-risk" designation under Annex III — it applies even to limited-risk systems.