What is the rebuttable presumption of causation?

Under Article 4 of the proposed AI Liability Directive, if a claimant shows the defendant was at fault and it is reasonably likely that fault influenced the AI output, the court presumes a causal link between the fault and the output. The burden then shifts to the defendant to rebut it, which is easier to trigger for high-risk systems.

How does evidence disclosure affect liability?

Article 3 lets courts order disclosure of evidence about high-risk AI systems suspected of causing damage. If a defendant refuses a disclosure order, a rebuttable presumption of non-compliance with a relevant duty of care arises, materially increasing exposure, so complete logging and documentation are protective.

Does the directive apply to minimal-risk AI?

The presumption of causation applies most readily to high-risk systems. For non-high-risk systems it applies only where the court considers it excessively difficult for the claimant to prove causation, so exposure is generally lower but not zero.

Is the AI Liability Directive in force?

It is a legislative proposal whose status has shifted during negotiations, so it may change or be withdrawn. This tool reflects the published proposal and gives a relative estimate, not a statement of binding law.

Is this legal advice?

No. It is an educational scoring aid that produces a relative exposure figure from a few inputs. Real liability depends on facts, jurisdiction, and the final text of any adopted instrument, so consult qualified counsel.

AI Liability Risk Scorer (EU AI Liability Directive)

The proposed EU AI Liability Directive makes it easier for people harmed by AI systems to bring civil claims, by easing their burden of proof. This scorer turns the directive’s key levers — risk tier, your role, the harm type, and your disclosure posture — into a single relative exposure figure so product and legal teams can compare scenarios.

How it works

The directive eases the claimant’s burden through two main mechanisms, both of which this tool weights:

Disclosure of evidence (Article 3) — courts can order disclosure of evidence about high-risk AI systems. Refusing a disclosure order triggers a rebuttable presumption of non-compliance with a relevant duty of care.
Rebuttable presumption of causation (Article 4) — once fault is shown and it is reasonably likely the fault influenced the AI output, causation between the fault and the output is presumed, shifting the burden to the defendant.

The presumption applies more readily to high-risk AI systems as classified by the AI Act, and is qualified for non-high-risk systems. The tool scores four inputs — risk tier, deployment role, harm type, and disclosure posture — sums their weights, and normalises the total to a 0 to 100 exposure figure with a band.

Example

A high-risk system operated by a professional deployer, where a user suffers a fundamental-rights harm and disclosure is contested, scores in the high band. The tool notes that the Article 4 presumption applies readily for high-risk systems and that contested disclosure invites adverse inferences.

Tips and notes

The directive is a proposal whose final form may differ, so treat the score as a relative comparison between scenarios rather than an absolute liability estimate. Complete logging, documentation, and readiness to disclose are the single most effective way to reduce the disclosure-driven component of exposure.