The advent of artificial intelligence (AI) has brought forth a myriad of opportunities and complexities for organizations across various sectors. Recognizing the transformative potential of AI in business operations, the British Standards Institution (BSI) has unveiled the "Little Book of AI." This comprehensive guide aims to assist organizations in understanding and implementing AI effectively, emphasizing responsible management and adherence to international standards.
The Growing Impact of AI:
AI has become prevalent in various sectors such as health, defense, transportation, finance, employment, and energy. Its ability to enhance performance and efficiency in existing functions presents economic benefits. Moreover, AI has a crucial role in environmental protection and sustainable practices, with 87% of climate and AI leaders acknowledging its utility in the fight against climate change. Examples include the integration of satellites and AI for surveillance and monitoring.
BSI's Little Book of AI: A Guide to Navigate AI Implementation:
Organizations find themselves at the crossroads of opportunity and complexity with AI. To aid in navigating these intricacies, BSI's Little Book of AI serves as an essential resource for businesses of all sizes and sectors. This guide addresses the opportunities and challenges associated with AI, providing industry-specific examples, benefits, and use cases in areas such as traffic management, smart agriculture, and automated facial recognition.
Key Components of the Little Book of AI:
Market Dynamics and Opportunities: The guide underscores the vast and growing AI market, highlighting the potential for opportunities and benefits if risks are managed responsibly.
ISO/IEC Standards: BSI emphasizes the significance of ISO/IEC standards in AI governance, management systems, and risk management. These standards provide a risk-based and flexible approach to ensure responsible AI implementation.
AI Management System Standard (ISO/IEC 42001): This standard is designed for compliance, conformity assessment, and certification, contributing to embedding trust in the supply chain.
Future Standards: The guide anticipates the publication of additional ISO/IEC standards in 2024, covering a range of technical topics related to AI.
Benefits of AI Standards:
BSI's Little Book of AI sheds light on industry-specific examples and outlines five key benefits of adhering to AI standards:
BSI's Role in AI Standards Development:
Founded in 1901, BSI is the UK's National Standards Body and has been actively involved in developing internationally-recognized AI standards for over five years. Collaborating with organizations of all sizes, academia, and civil society, BSI addresses the diverse challenges faced by enterprises.
Commitment to Business Sustainability:
BSI is committed to providing guidance and support to businesses through clear standards and consensus-led industry best practices. Business sustainability is a priority, aligning with the United Nations' Sustainable Development Goals (SDGs). BSI aims to help organizations understand and implement the SDGs, demonstrating a responsible approach to AI and fostering organizational innovation.
As organizations navigate the complex landscape of AI, BSI's Little Book of AI and adherence to ISO/IEC standards serve and several BS standards are invaluable resources. By promoting responsible AI implementation, BSI aims to contribute to the advancement of businesses while aligning with global sustainability goals.
International Standards related to AI
|1||ISO/IEC 42001, Information technology –Artificial intelligence – Management system|
|2||BS ISO/IEC 22989:2022, Information technology – Artificial intelligence – Artificial intelligence concepts and terminology|
|3||BS ISO/IEC 25059:2023, Software engineering – Systems and software Quality Requirements and Evaluation (SQuaRE) – Quality Model for AI systems|
|4||PD ISO/IEC TR 29119-11:2020, Software and systems engineering – Software testing – Part 11: Guidelines on the testing of AI-based Systems|
|5||BS 30440:2023, Validation framework for the use of artificial intelligence (AI) within healthcare – Specification|
|6||ISO/IEC TS 4213:2022, Information technology – Artificial intelligence – Assessment of machine learning classification performance|
|7||BS ISO/IEC 23894:2023, Information technology – Artificial intelligence – Guidance on risk management|
|8||BS ISO/IEC 38507:2022, Information technology – Governance of IT – Governance implications of the use of artificial intelligence by organizations|
|9||PD ISO/IEC TR 24028:2020, Information technology – Artificial intelligence – Overview of trustworthiness in artificial Intelligence|
|10||PD ISO/IEC TR 24027:2021, Information technology – Artificial intelligence (AI) – Bias in AI systems and AI aided decision making|
|11||ISO/IEC TR 24029-1:2021, Artificial Intelligence (AI) – Assessment of the robustness of neural networks – Part 1: Overview|
|12||PD ISO/IEC TR 24368:2022, Information technology – Artificial intelligence – Overview of ethical and societal concerns|
|13||BS ISO/IEC 23053:2022, Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML)|
|14||PD ISO/IEC TR 27563:2023, Security and privacy in artificial intelligence use cases. Best practices|
|15||BS/AAMI 34971:2023, Application of ISO 14971 to machine learning in artificial intelligence. Guide|
|16||BS ISO/IEC 24668:2022, Information technology. Artificial intelligence. Process management framework for big data analytics|
|17||IEC 62243:2012, Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE)|
|18||BS ISO/IEC 8183:2023, Information technology. Artificial intelligence. Data life cycle framework|
|19||PD IEC SRD 63416:2023, Ethical considerations of artificial intelligence (AI) when applied in the active living (AAL context)|
|20||ISO/IEC 2382-29:1999, Information technology. Vocabulary - Artificial Intelligence. Speech recognition and synthesis|
|21||ISO/IEC 2382-34:1999, Information technology. Vocabulary - Artificial Intelligence. Neural Networks|
|22||ISO/IEC 2382-31:1997, Information technology. Vocabulary. Part 31: Artificial intelligence. Machine learning|
|23||ISO/IEC 2382-28:1995, Information technology. Vocabulary. Part 28: Artificial intelligence. Basic concepts and expert systems|
|24||PD IEC TR 63468:2023, Nuclear facilities. Instrumentation and control, and electrical power systems. Artificial Intelligence applications|
|25||PD CEN ISO/TR 22100-5:2022, Safety of machinery. Relationship with ISO 12100 - Implications of artificial intelligence machine learning|
|26||PD ISO/IEC TR 20547-1:2020, Information technology. Big data reference architecture - Framework and application process|
|27||PD ISO/TS 5346:2022, Health Informatics - Categorial structure for representation of traditional Chinese Medicine clinical decision support system|
|28||PD ISO/TS 23535:2022, Health informatics. Requirements for customer-oriented health cloud service agreements|
|29||PD ISO/IEC TR 22981:2020, Information technology. Office equipment. Guidelines for the development of an ontology (vocabulary, components and relationships) for office equipment|
|30||BS EN ISO 9241-110:2020, Ergonomics of human-system interaction - Interaction principles|
|31||PD ISO/TR 23845:2020, Biomimetics. Ontology-Enhanced Thesaurus (OET) for biomimetics|
|32||PAS 1040:2019, Digital Readiness. Adopting digital technologies in manufacturing. Guide|
|33||BS EN ISO 13482:2014, Robots and robotic devices. Safety requirements for personal care robots|
|34||BS IEC SRD 63273-1:2023, Smart city use case collection and analysis. City information modelling - High-level analysis|
|35||PD CEN ISO/TR 9241-810:2022, Ergonomics of human-system interaction - Robotic, intelligent and autonomous systems|
|36||BS ISO 18646-4:2021, Robotics. Perfomance criteria ad related test methods for service robots - Lower-back support robots|
|37||PD IEC TR 62998-2:2020, Safety of machinery - Examples of application|
|38||PD ISO/TR 23482-1:2020, Robotics. Application of ISO 13482 - Safety-related test methods|
|39||PD ISO/TS 15066:2016, Robots and robotic devices. Collaborative robots|
|40||BS EN ISO 9409-2:2003, Manipulating industrial robots. Mechanical interfaces - Shafts|
|41||PD ISO/IEC TR 24372:2021, Information technology. Artificial intelligence (AI). Overview of computational approaches for AI systems|
|42||BS EN ISO 9409-1:2004, Manipulating industrial robots. Mechanical interfaces - Plates|
|43||23/30432295 DC, BS ISO/IEC 5259-2. Artificial intelligence. Data quality for analytics and machine learning (ML) - Part 2. Data quality measures|
|44||23/30424812 DC, BS ISO/IEC 5259-4, Artificial intelligence. Data quality for analytics and machine learning (ML) - Part 4. Data quality process framework|
|45||23/30424809 DC, BS ISO/IEC 5259-3. Artificial intelligence. Data quality for analytics and machine learning (ML) - Part 3. Data quality management requirements and guidelines|
|46||23/30424806 DC, BS ISO/IEC 5259-1. Artificial intelligence. Data quality for analytics and machine learning (ML) - Part 1. Overview, terminology, and examples|
|47||23/30426038 DC, BS ISO/IEC 5392. Information technology. Artificial intelligence. Reference architecture of knowledge engineering|
|48||22/30425900 DC, BS ISO/IEC 5338. Information technology. Artificial intelligence. AI system life cycle processes|
|49||22/30444391 DC, BS EN ISO/IEC 24029-2 Artificial intelligence (AI) - Assessment of the robustness of neural networks - Part 2: Methodology for the use of formal methods|
|50||22/30426041 DC, BS ISO/IEC 42001. Information Technology. Artificial intelligence. Management system|
|51||23/30470941 DC, BS EN ISO/IEC 4200. Information technology. Artificial intelligence. Requirements for bodies providing audit and certification of artificial intelligence management systems|
|52||23/30453500 DC, BS9347. Facial recognition technology. Ethical use and deployment in video surveillance-based systems. Code of practice|
|53||23/30472672 DC, BS EN IEC 63310. Functional performance criteria for robots used in AAL connected home environment|
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