ALGORITHMIC NORMS
An innovative legal theory that integrates Artificial Intelligence into judicial procedures, maintaining judicial autonomy and increasing institutional efficiency.
Traditional Dogmatics
Classic models with limited explanatory power for technical mediation.
Technical Infrastructure
Systems, rules and pipelines structuring institutional work.
Decision Flows
Triage, routing, timing and priorities shaping decisions.
Algorithmic Governance
Transparency, auditability and responsibility by design.
The scientific problem
Despite the advancements in electronic judicial proceedings, the Judiciary faces a critical paradox: digitalization has not eliminated delays; it has merely transferred them to the digital environment. Today, systems operate predominantly as document repositories, lacking an intelligent comprehension of legal content.
Current operating systems fail to address core issues related to: 1) Natural Language: legal texts are replete with ambiguities, contradictions, and critical nuances that remain unperceived by current systems; 2) Clerical Automation: the technological infrastructure requires clear signals to act, without which systems remain entirely dependent on court staff.
In the electronic process, technical infrastructures, automated workflows, and algorithmic classifications cease to be mere auxiliary instruments and begin to organize institutional operations: they modulate time, priority, and visibility while enhancing the coordination of procedural acts.
Natural Language Challenge
Legal texts with ambiguities, contradictions, and critical nuances unperceived by current systems
Clerical Automation Gap
Technical infrastructure dependent on clear signals, keeping staff overwhelmed with repetitive tasks
The concept of Algorithmic Norms
Algorithmic Norms are non-codified normative patterns embedded in computational systems that guide, condition or constrain institutional practice in digital environments. They are not legal text, yet they generate concrete organizational and procedural effects.
Second-Order Algorithmic Norms
Second-Order Algorithmic Norms emerge as the definitive bridge between judicial decisions and automation. By utilizing cognitive modeling based on Machine Learning, the proposed model diagnoses the linguistic signals within decisions; it translates these signals into automated operational commands for the operating system and ensures the operative closure of the Law, maintaining judicial autonomy while automating back-office activities.
Algorithmic norms represent an advancement for the Justice system by assigning the algorithm the function of interpreting legal texts and reducing procedural nullities, ensuring procedural integrity and efficiency.
Core Innovation
Bridges judicial decisions and automation through cognitive modeling, maintaining judicial autonomy while automating back-office activities
Machine Learning Approach
Diagnoses linguistic signals in decisions and translates them into automated operational commands
Flow (macro view)
System Architecture
Flows & Subflows
Classification & Prioritisation
Decision Conditions
Theoretical foundations
The framework treats law and technical systems as coupled domains: normativity emerges from operational coordination and technology-mediated institutional practice.
Structural Coupling
Law and technical systems as coupled domains with mutual influence and coordination
Emergent Normativity
Norms arising from operational coordination rather than explicit codification
Legal Meta-operation
Second-order operations that shape how legal systems process information
Technology & Law
Contemporary courts explained without reducing to 'legal text' or 'mere efficiency'
Practical Applications
Concrete implementations of Algorithmic Norms across different dimensions of the justice system
Electronic Procedure
Flow/subflow design with traceability and accountability. Algorithmic norms structure procedural workflows, ensuring transparent automation while maintaining auditability at every stage of the electronic judicial process.
- •Automated workflow orchestration
- •Intelligent petition triage
- •Procedural deadline control
- •Complete action traceability
Judicial Governance
Criteria, controls and accountability for institutional algorithmic decisions. Governance structures that ensure human oversight, transparency and accountability in the implementation of automated systems.
- •Accountability frameworks
- •Algorithmic audits
- •Transparency protocols
- •Continuous human oversight
Responsible AI
Audit, transparency, explainability and normative limits for AI systems in the judicial context. Implementation of ethical and legal principles in artificial intelligence systems applied to Law.
- •Decision explainability
- •Algorithmic bias detection
- •LGPD compliance
- •Ethics in judicial AI
Institutional Efficiency
Process management optimization without compromising guarantees. Automation of repetitive clerical tasks, allowing staff and magistrates to focus on higher-value activities.
- •Reduction of repetitive tasks
- •Human resource optimization
- •Process acceleration
- •Maintenance of procedural guarantees
Who should care
Academia
Researchers in Law, Computer Science and related fields find here a robust theoretical framework to study the intersection between normativity and technology.
Scientific PublicationsCourts
Magistrates and judicial managers can understand how algorithmic norms already operate in their systems and how to govern them transparently and responsibly.
Institutional ImplementationLegalTech
Legal technology companies find theoretical and practical foundations to develop solutions that respect the autonomy of Law and procedural guarantees.
Partnership OpportunitiesFounder
Jefferson Germinari, PhD
Doctor of Law (UFSC)
São Paulo Court of Justice
AI & Law · Digital Justice · Legal Theory
Leading researcher in the field of Algorithmic Norms, developing innovative legal theory on the intersection between artificial intelligence, judicial procedure and algorithmic governance.
Contact & Partnerships
Academic invitations, institutional projects, lectures and scientific cooperation. Send a message and describe the context.