Algorithmic Transparency
The disclosure of how AI systems make decisions and process information.
How the laws define this
Regulators uniformly agree that algorithmic transparency requires making an AI system's decision logic, data, and outputs understandable and explainable to affected individuals and oversight bodies. Each framework establishes an obligation to disclose clear information so stakeholders can comprehend and potentially challenge automated recommendations or decisions. The key point of divergence lies in the administrative and compliance mechanisms required to fulfill this mandate. Principles-based frameworks like the San Juan AI Judicial Protocol and the Национальный этический кодекс в сфере искусственного интеллекта focus broadly on making system logic, data, and outcomes accessible. In contrast, operational measures like Law No. 31814 — Law that promotes the use of Artificial Intelligence in favor of the country's economic and social development (Ley que promueve el uso de la inteligencia artificial), House Bill No. 7396 (Artificial Intelligence Development and Regulation Act), and the Artificial Intelligence Technologies and Recordkeeping Policy enforce explicit administrative burdens, requiring formal documentation, parameter tracking, algorithmic impact assessments, or heightened disclosures for high-risk systems.
Synthesised from the 6 statutory definitions below. Each one is quoted in full, with its source.
How jurisdictions differ
What is distinctive in each definition, binding law in force first. Showing 4 of the 6 instruments that define this term; all are listed under Definitions below.
| Jurisdiction | Instrument | Status | What is distinctive |
|---|---|---|---|
| San Juan, Argentina | San Juan AI Judicial Protocol | In Force | This definition frames algorithmic transparency as a principle emphasizing understandability and accessibility of AI logic and data specifically to relevant stakeholders. |
| Victoria, Australia | Artificial Intelligence (AI) Technologies and Recordkeeping Policy | In Force | This definition widens transparency to include recording details on AI models' design, parameters, modifications, and explanations of decision processes for complex systems to ensure recordkeeping. |
| Kazakhstan | National Ethical Code in the Field of Artificial Intelligence | In Force | This definition narrows transparency to developers and operators providing clear information about AI logic, data, and intended outcomes. |
| Philippines | House Bill No. 7396 (Artificial Intelligence Development and Regulation Act) | Under Review | This definition expands transparency to the degree of understandability of operating logic, processes, and factors, serving as basis for mandatory disclosure, explanation, and documentation especially for high-risk systems. |
What it triggers
- In San Juan, Argentina, algorithmic transparency under the San Juan AI Judicial Protocol mandates human oversight and transparency for AI tools used by judges and judicial staff.source
- In Victoria, Australia, the Artificial Intelligence (AI) Technologies and Recordkeeping Policy sets out that algorithmic transparency involves recording details about AI models and algorithms for public offices managing AI-generated content as public records.source
- Under Kazakhstan's National Ethical Code in the Field of Artificial Intelligence, algorithmic transparency calls for developers and operators to provide clear information about AI systems.source
Definitions (6)
Algorithmic transparency refers to the capacity to understand, explain, and scrutinize how AI systems reach conclusions or recommendations; it requires disclosure of AI use and explanation of AI-assisted outputs to affected parties to enable oversight, challenge, and public accountability.
Obligations for developers and deployers to provide clear documentation, explanations, and transparency statements about model functionality, decision logic, and potential impacts whenever decisions affect individuals; includes requirements for algorithmic impact assessments and disclosures to enable understanding and challenge by affected persons.
The degree to which an AI system's operating logic, decision-making processes, and influencing factors can be understood and explained to affected individuals and overseers, forming the basis for mandatory disclosure, explanation, and documentation requirements particularly for high‑risk systems.
Algorithmic transparency requires recording details about AI models and algorithms, including their design, parameters, and modifications. For complex systems, it means capturing explanations of how decisions are reached, ensuring understandability and explainability.
The principle that the logic and data used by an AI system should be understandable and accessible to relevant stakeholders.
This involves the obligation of developers and operators to provide clear information about the logic, data, and intended outcomes of AI systems.
Not to be confused with
Explainability focuses on the ability to understand and articulate how an AI system reaches decisions, while algorithmic transparency encompasses broader disclosure and accessibility of logic and data.
Human oversight involves mechanisms for human control and intervention over AI operations, whereas algorithmic transparency concerns disclosure and understandability of AI decision-making processes.
Questions and answers
What does algorithmic transparency require for judicial AI in San Juan?
It requires the logic and data used by AI systems to be understandable and accessible to relevant stakeholders, mandating human oversight and transparency for tools used by judges.
How does Victoria's AI policy address algorithmic transparency?
It sets out recording details about AI models, algorithms, design, parameters, modifications, and explanations of decisions for complex systems to ensure understandability in public recordkeeping.
What obligations does the Philippines bill impose for high-risk AI?
It bases mandatory disclosure, explanation, and documentation requirements on the degree to which an AI system's logic, processes, and factors can be understood, particularly for high-risk systems.
Related Terms
Explainability
The ability to understand and articulate how an AI system reaches its decisions....
Limited-Risk AI System
AI systems with specific transparency obligations, such as chatbots and emotion recognition....
Human Oversight
Mechanisms enabling human control and intervention over AI system operations....