Input data and information sources
The parties' lawyers write pleadings, often using their own templates in mass litigation. Pleadings include parties, addresses, vehicles or flight details.
AI assistance in high-volume court proceedings
Also known as: AI assistance in courts for mass litigation
In high-volume proceedings, the system organises pleadings, extracts each case's features and prepares draft decisions.
Understand the use case
What defines this use case?
For cases such as diesel emissions claims or airline compensation claims, it extracts the few differing details and selects appropriate standard text passages. Stuttgart Higher Regional Court uses such a system; four German states are jointly trialling another.
This page describes a typical system design. Actual data flows and permissions depend on your implementation.
- System design
- Analysis and processing
- Ability to act
- Action permissions depend on integration
- Fields of use
- judiciary
- Also known as
- AI in mass litigation, judgment drafting tool
How it works, step by step
System design and data flows
Input data
Claims, responses and attachments arrive through the electronic case file.
The parties' lawyers write pleadings, often using their own templates in mass litigation. Pleadings include parties, addresses, vehicles or flight details.
Processing
The system classifies pleadings by proceeding type and extracts decision-relevant features.
The system prepares pleadings and case information and identifies recurring topics or comparable circumstances. It can support grouping and preparing similar proceedings. An automatically generated case pattern and the legal assessment of an individual case serve different purposes.
Results
The system displays case features and suggests standard text passages or a draft decision.
The result describes the defined model task. A match, category or forecast acquires its professional significance only in conjunction with the underlying data and intended use.
Use and impact
Judges review the proposal, decide the case and take responsibility for the judgment.
Integration determines whether the result is simply displayed, adopted after approval or written directly into a business application. These variants differ in their data flows and permissions.
Explore the system design: Select a component to see its task, incoming and outgoing data, and role in the use case. The guided walkthrough explains each phase. Then use Yes, Uncertain and No to represent your specific system variant.
6 review first18 clarify6 baseline threats
- Present
- Depends on your system
- Absent
- Review first
- To clarify
How to use the model
The diagram shows the typical architecture. Select a component to see what happens there and adapt its features to your system. Enable “Analysis guidance” to see which threats need reviewing for this architecture. The page's checklist continues to describe the typical case.
Changes from the typical case
None yet. Each change will appear here.
Where most threats arise
- 5 clarify
- 5 clarify
- 4 review first
- 3 clarify
In this system design: 6 to review first, 18 to clarify, 6 baseline threats.
Architecture as a list
External access: Depends on your system
To clarify
Input: Part of every AI system
Review first
Third-party content: Present
AI model: Part of every AI system
To clarify
- Sleepy Agent (Time/Event-Triggered Hidden Instructions)
- Prompt Injection: Direct
- Prompt Injection: Indirect
- Jailbreaks
- Meta Prompt Extraction
Baseline for every AI system
Provider service: Depends on your system
To clarify
Document collection: Depends on your system
To clarify
Training and feedback: Depends on your system
To clarify
Result: Part of every AI system
Review first
- Insecure Output Handling Review priority
To clarify
Business application or equipment: Depends on your system
To clarify
person: Part of every AI system
Review first
- Overreliance Review priority
Affected person: Present
Unapproved AI services: Depends on your system
To clarify
Supply chain and technical foundations: 5 baseline threats applying to every AI system
Baseline for every AI system
How it differs from similar use cases
Intelligent document processing (IDP)
Routes incoming pleadings to the correct file and transfers information
AI knowledge assistant (enterprise search)
Answers questions about case law and literature from a fixed collection
Data and information
Processing and operating environment
The model processes the inputs. Whether it runs internally or with a service provider must be established for the specific implementation.
Storage and further use
Some systems retrieve similar cases that have already been decided. Case-feature classification models are often trained on pleadings from the court itself.
Results and data sharing
Depending on integration, the system writes features into the electronic file itself or only displays them. If a cloud service hosts a language model, parts of case files leave the court.
Using a provider does not automatically determine where processing takes place. Storage location, subcontractors, deletion periods and use for training must be checked for the specific deployment.
Assess risks and determine measures
From understanding the system to analysing risks
- Define the system boundary
Record data, participants, interfaces and permissions. Resolve uncertain characteristics in the model.
- Investigate threats
Relate review candidates to actual data flows: what could happen, and who would be affected?
- Assess risk
Assess impacts and likelihood in the deployment context. The number of review candidates is not a risk score.
- Provide evidence of measures
Select appropriate controls, assign responsibilities and define effectiveness checks. Document residual risks and approvals.
Key threats in the application context
- 3 particularly important
- 3 typical
- 18 depending on the system
- 6 additional threats for every AI system
A total of 30 threats from the threat catalogue may be relevant to this use case.
Also review: whether the system disadvantages groups
Check whether selection, ranking or assessment disadvantages people with protected characteristics, such as sex, age or origin.
Priority review points
With many similar claims, simply accepting extracted features and drafts is tempting. If the system misses a distinguishing detail, the judgment does not fit the case.
Extracted features such as vehicle type, purchase date or flight delay select the draft's standard text passages. A misread feature inserts the wrong legal reasoning.
Law firms handling mass claims can draft pleadings to make the system recognise or overlook particular features. Preparation then shifts in one party's favour.
3 further review candidates in the typical design
Other relevant threats
Targeted queries can recover some personal or protected training data from the model.
Listed because of these characteristics
- Processes personal data
- Processes confidential data
Sensitive Information Disclosure
Confidential content can appear in answers, supplied context or logs.
Listed because of these characteristics
- Processes personal data
- Processes confidential data
Exfiltration from ML Application
Protected data can leak through tools, network connections, storage or insiders.
Listed because of this characteristic
- Processes confidential data
Up to 18 further threats may apply, depending on your system design. Nine threats need to be reviewed for every AI system. Three of these are particularly important here and are therefore listed above.
System features and open questions
Each Yes adds further threats to your checklist, up to 18 in total. If you cannot yet answer a question, include its threats in your review.
- Added by this Yes
- Also requires a Yes to another question
How your system is built
- Performs natural language tasks5 threats
- Generates its own text2 threats, plus 1 only in combination with other characteristics
- Learns from your own data2 threats, plus 1 only in combination with other characteristics
- Uses operational feedback2 threats
- Searches its own document collection1 threat
- Formulates answers from retrieved documents1 threat
- Changes data or triggers processes1 threat
- Uses another provider's model1 threat
- Learns from examples with correct answers1 threat
How you operate the system
- Accepts queries directly through an interface4 threats, plus 1 only in combination with other characteristics
- Accessible over a network2 threats, plus 1 only in combination with other characteristics
- Incurs costs with each query1 threat
- Shares computing resources with other customers or applications1 threat
- Used alongside unapproved AI services1 threat
How your system is built
Does the system generate its own text, such as answers, drafts or summaries?
The system assembles drafts from standard text passages without generating wording; a language model writes its own sentences.
If yes, add
Can users give the system natural language tasks that it then performs?
If a language model is involved, it follows instructions, including ones within pleadings.
If yes, add
Does the system search its own collection of documents or records for relevant content?
Some systems retrieve similar cases that have already been decided.
If yes, add
Does the system formulate answers from retrieved documents?
If yes, add
Can the system change things itself, such as storing data, sending messages, initiating transactions or granting permissions?
Depending on integration, the system writes features into the electronic file itself or only displays them.
If yes, add
Does your organisation train or fine-tune the model itself using its own data?
Case-feature classification models are often trained on pleadings from the court itself.
If yes, add
Does the model or its base model come from another provider, even if you host it?
If yes, add
Is the model trained on examples to which someone has assigned the correct answer?
Learning to recognise case features requires pleadings in which people have marked those features.
If yes, add
Does operational feedback or output later feed into analysis or training?
If yes, add
How you operate the system
These questions arise for almost every AI system.
Can people or programs query the model directly, for example through an interface?
If yes, add
Is the system accessible over a network or dependent on network services?
If yes, add
Do you pay per query or unit of computing time, so costs increase with use?
If yes, add
Does the system share servers or computing resources with other customers or applications?
If yes, add
Do staff also use AI services for this task that your organisation has not approved?
Staff who copy case-file material into a freely available chat service do so without approval.
If yes, add
Controls and implementation
Measures for the priority review areas are expanded. Each linked measure explains how to implement it.
Review priorityOverreliance3 measures
Review priorityInsecure Output Handling3 measures
Review priorityAdversarial Inputs2 measures
Privacy Attacks3 measures
Sensitive Information Disclosure3 measures
Exfiltration from ML Application3 measures
Controls for the remaining threats are available on their pages in the threat catalogue.
AI Act provisions
-
Art. 4: AI literacy
For every AI system Applies since 2 February 2025.
-
Art. 53 para. 1 point b, Annex XII: Embedded general-purpose model
Depending on use Review trigger since 2 August 2025.
-
Annex III no. 8 point a: Administration of justice
In typical use Review trigger from 2 December 2027.
Applies to every AI system
Art. 4: AI literacy
Applies if staff or appointed representatives operate or use AI systems.
Applies since 2 February 2025.
Obligations and details for Art. 4: AI literacy
- Who bears the obligations
- Provider, Deployer, Product manufacturer, Provider of a general-purpose AI model
Detailed obligations
May apply in typical use
Annex III no. 8 point a: Administration of justice
Applies if the system helps judges research facts or interpret and apply legal provisions, for example by preparing draft decisions.
Applies from 2 December 2027.
Obligations and details for Annex III no. 8 point a: Administration of justice
- The general condition is
- Applies if the system helps courts or dispute resolution bodies establish facts and interpret law.
- Exception
- Purely ancillary tasks such as anonymising judgments or staff communication are excluded under recital 61.
- Who bears the obligations
- Provider, Deployer, Product manufacturer
Detailed obligations
May apply in particular uses
Art. 53 para. 1 point b, Annex XII: Embedded general-purpose model
Applies if a large language model or another third-party general-purpose model analyses pleadings or drafts decisions.
Applies since 2 August 2025.
Obligations and details for Art. 53 para. 1 point b, Annex XII: Embedded general-purpose model
- The general condition is
- Applies if another provider's general-purpose AI model is incorporated, such as a large language model.
- Points to consider
- The model provider bears its obligations. It must give you the Annex XII information; use it for your own documentation.
- Who bears the obligations
- Provider of a general-purpose AI model
Detailed obligations
Classify your system with the quick checker All obligations in the obligations compass
Cross-cutting threats
Review these threats for every AI system, whatever its purpose.
- Supply Chain: Infrastructure
- Supply Chain: Models
- Supply Chain: Datasets
- Adversarial Inputs Review priority: Adversarial Inputs
- Insecure Output Handling Review priority: Insecure Output Handling
- Application Vulnerabilities
- Overreliance Review priority: Overreliance
- Model Drift & Degradation
- Middleware Exploits (AI Framework Attacks)
Atypical system features
View 14 further questions
If any of these features does apply to your system, add the listed threats.
Does the system generate or edit images, sounds, voices or videos?
If yes, add
Does the system create new content from open-ended requests that people read, see or hear directly? Translation, reading aloud and transcription do not count.
Judges review and take responsibility for the draft; the system publishes nothing itself.
If yes, add
Does the system use a knowledge graph storing concepts and their relationships?
If yes, add
Does the system remember content from earlier conversations or tasks and reuse it later?
If yes, add
Does the system call other programs, interfaces or search services itself, even if only to read?
If yes, add
Does the system determine its next steps itself based on earlier results?
If yes, add
Does the system delegate tasks to other AI agents or incorporate their results?
If yes, add
Is the system connected to tools or data sources through the Model Context Protocol (MCP)?
If yes, add
Do documents from several customers, departments or permission groups share one search store?
If yes, add
Can people outside your organisation use the system, including without login?
Only court staff use the system.
If yes, add
Does the system process several input types together, such as text with images or speech?
If yes, add
Does the system generate code, scripts or database queries for further use?
If yes, add
Does the system execute programs, scripts or commands itself?
If yes, add
Is the system intended to maximise a specified metric, such as revenue, utilisation or clicks?
If yes, add
Sources
- Report of a deployment
LTO, OLGA, FRAUKE & Co.: AI in the judiciary, 14 July 2023
The report describes OLGA at Stuttgart Higher Regional Court and an eight-week FRAUKE feasibility trial in 2021.
- Legislation, norm or standard
EU AI Act, Annex III, point 8(a)
The Act expressly lists AI systems supporting judicial authorities in researching and interpreting facts and law in Annex III.
- Report of a deployment
Lower Saxony Ministry of Justice, Cooperation on MAKI AI assistance software, 7 July 2025 According to the deployer
The ministry describes four states' collaboration: MAKI extracts information from pleadings and manages standard text passages but does not make decisions itself.
Related use cases
Also in the field of Administration, justice and public safety
- Risk-based review selection for applications and benefitsAuthorities assess applications or payments for error or fraud risk and use this to select cases for closer review.
- Automated property valuation (AVM)A model estimates residential property market values from property characteristics and comparable sales.
- Police data analysis platformsPolice analysis platforms combine data from different sources and display links between people, incidents and cases as graphs, tables or maps.
- Language and dialect recognition in asylum proceduresFrom a short speech sample, the system estimates which language and dialect an asylum seeker is likely to speak.
- Automatic number plate recognition (ANPR)Cameras capture vehicle number plates, and the system automatically checks them against registration or police search data.
- Intelligent video analytics in public spacesThe system detects specific events or objects in camera images and alerts a control centre without identifying people.
Often used together with
Updated: 30 September 2026. Review checklist rules version 0.3.2, threat catalogue version 2026.10.24.2. How these pages are structured