Information technology — Artificial intelligence — Assessment of machine learning classification performance

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 1: Overview, terminology, and examples

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 2: Data quality measures

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 3: Data quality management requirements and guidelines

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 4: Data quality process framework

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 5: Data quality governance framework

Artificial intelligence — Data quality for analytics and machine learning (ML) — Part 6: Visualization framework for data quality

Information technology — Artificial intelligence — AI system life cycle processes

Information technology — Artificial intelligence — Guidance for AI applications

Information technology — Artificial intelligence — Reference architecture of knowledge engineering

Artificial intelligence — Functional safety and AI systems

Information technology — Artificial intelligence — Objectives and approaches for explainability and interpretability of ML models and AI systems

Information technology — Artificial intelligence — Data life cycle framework

Information technology — Artificial intelligence — Controllability of automated artificial intelligence systems

Information technology — Artificial intelligence — Treatment of unwanted bias in classification and regression machine learning tasks

Information technology — Artificial intelligence — Transparency taxonomy of AI systems

Information technology — Artificial intelligence — Verification and validation analysis of AI systems

Information technology — Artificial intelligence — Overview of machine learning computing devices

Artificial intelligence — Application of AI technologies in health informatics

Information technology — Artificial intelligence — Environmental sustainability aspects of AI systems

Information technology — Big data — Overview and vocabulary

Information technology — Big data reference architecture — Part 1: Framework and application process

Information technology — Big data reference architecture — Part 2: Use cases and derived requirements

Information technology — Big data reference architecture — Part 3: Reference architecture

Information technology — Big data reference architecture — Part 5: Standards roadmap

Information technology – Artificial intelligence – Beneficial AI systems

Artificial intelligence — Functional safety and AI systems — Requirements

Information technology — Artificial intelligence — Guidance on addressing societal concerns and ethical considerations

Information technology — Artificial intelligence — Artificial intelligence concepts and terminology

Information technology — Artificial intelligence — Artificial intelligence concepts and terminology — Amendment 1

Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)

Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML) — Amendment 1

Artificial intelligence — Overview of AI tasks and functionalities related to natural language processing

Artificial Intelligence — Evaluation methods for accurate natural language processing systems

Information technology — Artificial intelligence — Guidance on risk management

Information technology — Artificial intelligence (AI) — Bias in AI systems and AI aided decision making

Information technology — Artificial intelligence — Overview of trustworthiness in artificial intelligence

Artificial Intelligence (AI) — Assessment of the robustness of neural networks — Part 1: Overview

Artificial intelligence (AI) — Assessment of the robustness of neural networks — Part 2: Methodology for the use of formal methods

Artificial intelligence (AI) — Assessment of the robustness of neural networks — Part 3: Methodology for the use of statistical methods

Information technology — Artificial intelligence (AI) — Use cases

Information technology — Artificial intelligence (AI) — Use cases

Information technology — Artificial intelligence — Overview of ethical and societal concerns

Information technology — Artificial intelligence (AI) — Overview of computational approaches for AI systems

Information technology — Artificial intelligence — Process management framework for big data analytics

Artificial intelligence — AI system logging

Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Guidance for quality evaluation of artificial intelligence (AI) systems

Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality model for AI systems

Software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Quality model for AI systems

Software and systems engineering — Software testing — Part 11: Testing of AI systems

Information technology — Governance of IT — Governance implications of the use of artificial intelligence by organizations

Information technology — Artificial intelligence — Management system

Information technology — Artificial intelligence — AI system impact assessment

Information technology — Artificial intelligence — Requirements for bodies providing audit and certification of artificial intelligence management systems

Information technology — Artificial intelligence — Taxonomy of AI system methods and capabilities

Information technology — Artificial intelligence — Overview of synthetic data in the context of AI systems

Information technology — Artificial intelligence — Guidance for human oversight of AI systems

Information technology — Artificial intelligence — Overview of differentiated benchmarking of AI system quality characteristics