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Standard and/or project under the direct responsibility of ISO/IEC JTC 1/SC 42 Secretariat | Stage | ICS |
---|---|---|
Artificial intelligence — Performance measurement for AI classification, regression, clustering and recommendation tasks
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20.00 |
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Information technology — Artificial intelligence — Assessment of machine learning classification performance
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90.92 | |
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 1: Overview, terminology, and examples
|
60.60 | |
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures
|
60.00 | |
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 3: Data quality management requirements and guidelines
|
60.60 | |
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 4: Data quality process framework
|
60.60 | |
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 5: Data quality governance framework
|
50.20 | |
Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 6: Visualization framework for data quality
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30.00 |
|
Information technology — Artificial intelligence — AI system life cycle processes
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60.60 | |
Information technology — Artificial intelligence — Guidance for AI applications
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60.60 | |
Information technology — Artificial intelligence — Reference architecture of knowledge engineering
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60.60 | |
Artificial intelligence — Functional safety and AI systems
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60.60 | |
Information technology — Artificial intelligence — Objectives and approaches for explainability and interpretability of ML models and AI systems
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50.00 | |
Information technology — Artificial intelligence — Data life cycle framework
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60.60 | |
Information technology — Artificial intelligence — Controllability of automated artificial intelligence systems
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60.60 | |
Information technology — Artificial intelligence — Treatment of unwanted bias in classification and regression machine learning tasks
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60.60 | |
Information technology — Artificial intelligence — Transparency taxonomy of AI systems
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40.60 | |
Information technology — Artificial intelligence — Verification and validation analysis of AI systems
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30.20 |
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Information technology — Artificial intelligence — Overview of machine learning computing devices
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60.60 | |
Artificial intelligence — Application of AI technologies in health informatics
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20.00 |
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Information technology — Artificial intelligence — Environmental sustainability aspects of AI systems
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50.00 | |
Information technology — Big data — Overview and vocabulary
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90.60 | |
Information technology — Big data reference architecture — Part 1: Framework and application process
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60.60 | |
Information technology — Big data reference architecture — Part 2: Use cases and derived requirements
|
60.60 | |
Information technology — Big data reference architecture — Part 3: Reference architecture
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60.60 | |
Information technology — Big data reference architecture — Part 5: Standards roadmap
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60.60 | |
Information technology – Artificial intelligence – Beneficial AI systems
|
30.60 |
|
Artificial intelligence — Functional safety and AI systems — Part 1: Requirements
|
20.00 |
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Artificial intelligence — Functional safety and AI systems — Part 2: Guidance
|
20.00 |
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Artificial intelligence — Functional safety and AI systems — Part 3: Examples of application
|
20.00 |
|
Artificial intelligence — Functional safety and AI systems — Requirements
|
20.98 |
|
Information technology — Artificial intelligence — Guidance on addressing societal concerns and ethical considerations
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20.00 |
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Artificial intelligence — Concepts and terminology — Part 2: Healthcare
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20.00 |
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Information technology — Artificial intelligence — Artificial intelligence concepts and terminology
|
60.60 | |
Information technology — Artificial intelligence — Artificial intelligence concepts and terminology — Amendment 1
|
30.20 | |
Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)
|
60.60 | |
Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML) — Amendment 1
|
30.20 | |
Artificial intelligence — Overview of AI tasks and functionalities related to natural language processing
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20.00 |
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Artificial Intelligence — Evaluation methods for accurate natural language processing systems
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20.00 |
|
Information technology — Artificial intelligence — Guidance on risk management
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60.60 | |
Information technology — Artificial intelligence (AI) — Bias in AI systems and AI aided decision making
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60.60 | |
Information technology — Artificial intelligence — Overview of trustworthiness in artificial intelligence
|
60.60 | |
Artificial Intelligence (AI) — Assessment of the robustness of neural networks — Part 1: Overview
|
60.60 | |
Artificial intelligence (AI) — Assessment of the robustness of neural networks — Part 2: Methodology for the use of formal methods
|
60.60 | |
Artificial intelligence (AI) — Assessment of the robustness of neural networks — Part 3: Methodology for the use of statistical methods
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20.00 |
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Information technology — Artificial intelligence (AI) — Use cases
|
95.99 | |
Information technology — Artificial intelligence (AI) — Use cases
|
60.60 | |
Information technology — Artificial intelligence — Overview of ethical and societal concerns
|
60.60 | |
Information technology — Artificial intelligence (AI) — Overview of computational approaches for AI systems
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60.60 | |
Information technology — Artificial intelligence — Process management framework for big data analytics
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60.60 | |
Artificial intelligence — AI system logging
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20.00 |
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Artificial intelligence — AI-enhanced nudging
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20.00 |
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Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Guidance for quality evaluation of artificial intelligence (AI) systems
|
60.60 | |
Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality model for AI systems
|
90.92 | |
Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality models for AI systems
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20.00 |
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Information technology — Artificial intelligence — Guidance and requirements for uncertainty quantification in AI systems
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20.00 |
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Information technology — Artificial intelligence — Hybrid AI inference framework for AI systems
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20.00 |
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Information technology — Artificial intelligence — Overview of data profiles for analytics and ML
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20.00 |
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Information technology — Governance of IT — Governance implications of the use of artificial intelligence by organizations
|
60.60 | |
Information technology — Artificial intelligence — Management system
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60.60 | |
Information technology — Artificial intelligence — AI system impact assessment
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50.00 | |
Information technology — Artificial intelligence — Requirements for bodies providing audit and certification of artificial intelligence management systems
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40.99 | |
Information technology — Artificial intelligence — Taxonomy of AI system methods and capabilities
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20.00 |
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Information technology — Artificial intelligence — Overview of synthetic data in the context of AI systems
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20.00 |
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Information technology — Artificial intelligence — Guidance for human oversight of AI systems
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20.00 |
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Information technology — Artificial intelligence — Overview of differentiated benchmarking of AI system quality characteristics
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20.00 |
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Information technology — Artificial intelligence — Use cases of human-machine teaming
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20.00 |
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Information technology — Artificial intelligence — Guidance on lightweight AI systems
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20.00 |
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Information technology — Artificial intelligence — Guidance on machine learning model training efficiency optimisation
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20.00 |
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Artificial intelligence — Testing of AI — Part 2: Overview of testing AI systems
|
20.00 |
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