Concern 861 · Working draft · editorial review in progress
AI liability and access to evidence in the EU
The withdrawn EU AI Liability Directive highlights a continuing question: how can people obtain evidence and prove responsibility when AI contributes to harm?
Understanding the concern
An injured person may know the outcome but lack access to the model, logs or internal decisions needed to explain it. The proposed directive aimed to address parts of this evidence problem in certain civil claims. It should not be confused with the separate AI Act or Product Liability Directive.
The European Commission withdrew the proposal in October 2025. It therefore does not give people the rights its draft once described. Imagine trying to show why a machine failed while all its records sit with the maker: existing legal routes may help, but the answer depends on the applicable rules. The unresolved concern is access to a workable remedy, not compliance with a law that never took effect.
An example
A person harmed by an AI-assisted service finds an old article describing proposed evidence rights and assumes they are already available.
Illustrative unless identified as a documented case.
Words in plain English
- AI
- Software that learns patterns from examples or uses rules to perform tasks such as recognizing images or generating text. It can make mistakes and does not understand every situation as a person would.
- Model
- The learned part of an AI system. It uses patterns found during training to produce an answer or prediction.
- EU
- The European Union, a group of European countries with shared institutions and some shared laws.
Evidence and its limits
EUR-Lex records withdrawal on 6 October 2025. The abandoned proposal must not be described as binding EU law or as eliminating other existing remedies.
European Union · Legislative record
Legislative procedure 2022/0303/COD: proposal withdrawn ↗ (opens in a new tab)AI Liability Directive proposal withdrawn on 6 October 2025. It is not enacted law; other liability routes remain separate.
Broader frameworks and background
These resources provide context. They do not establish every claim in this concern.
- EU AI Act — high-level summary (opens in a new tab)
regulation
- Council of Europe AI Convention (opens in a new tab)
regulation
- International humanitarian law (IHL)
framework
The body of law that governs the conduct of armed conflict and the protection of people who are not fighting, which applies to AI used in or for conflict.
A question to discuss
What evidence can the person actually obtain under current law, and where do practical gaps remain?
Keep track of different views, what needs checking, and any next step in your own discussion notes.
Open discussion notes →Principles for the conversation
Before you finish · reflect together
Virtues and principles to consider
Virtues are qualities we practice in how we treat people and make choices. They are not scores for judging someone. Use these ideas to explore what a thoughtful response could look like.
Justice
Justice means fair treatment, fair procedures, and attention to who receives benefits and burdens. It asks whether people with less power can be heard and seek repair.
Look at outcomes across different groups and individual cases. Provide a usable way to challenge decisions and correct harm. Equal wording in a policy does not guarantee equal treatment in practice.
Ask together: Who benefits, who bears the cost, and who can challenge the decision?
Accountability
Accountability means being answerable for decisions and taking responsibility for repair. It requires more than saying that the software made the choice.
Name the person or team responsible, keep useful records, and give them authority to correct problems. Make the route to a complaint clear to the people affected.
Ask together: Who will answer for the outcome and help put things right?
Dignity
Dignity means recognizing each person's worth regardless of usefulness, status, or a system's score. People deserve respect and a voice in matters that affect them.
Explain important decisions, avoid humiliating treatment, and keep room for a person's story. Do not treat a prediction as a complete account of who someone is.
Ask together: Does this use treat each person as someone whose experience matters?
Editorial ratings
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