En las contribuciones anteriores, Estándares ISO relacionados con el gobierno, la gestión y la calidad de la IA (I) y (II), presentamos las normas que abordan temas relacionados con el gobierno y la gestión de la IA. En esta ocasión abordaremos las normas [1] relacionadas con la calidad de los SIA, empezando por las normas…

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The European Artificial Intelligence Regulation has identified the reporting of serious incidents as one of the key responsibilities associated with high-risk AI systems. However, fulfilling this obligation is not merely a matter of reporting that an incident has occurred. It also requires having sufficient information to reconstruct what happened, assess its consequences and prevent it from happening again.

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In the previous post, ISO standards relating to the governance, management and quality of AI (I), we presented the standards that address topics relating to AI governance. On this occasion, we will look at the standards relating to organisational management of AI, which should not be confused with AI governance.

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Artificial Intelligence (AI) is having an ever-greater impact on people’s daily lives and plays a key role in the digital transformation of businesses and public administrations, thanks to its ability to automate and facilitate decision-making. Consequently, practices are required to govern, manage and ensure the quality of Artificial Intelligence Systems.

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The European landscape for artificial intelligence standardisation is at a particularly significant juncture. Following the publication of ISO/IEC 42001 as the first international standard for artificial intelligence management systems, CEN/CENELEC has published EN 18286 as a European standard aimed at quality management systems for regulatory purposes...

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The European organisations CEN (European Committee for Standardisation) and CENELEC (European Committee for Electrotechnical Standardisation), as well as ETSI (European Telecommunications Standards Institute), are developing harmonised standards that support the AI Act, and which include best practices that may be of interest to organisations wishing to govern, manage and ensure the quality of their Artificial Intelligence systems.

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As AI researchers, we are often seduced by near-perfect internal validation metrics. In fact, today’s deep learning models (including large language models) are notable for the remarkable accuracy they can achieve on specific tasks. However, the fact that they get a lot right does not necessarily mean that the patterns they have learnt are reliable or that they have actually «understood» the root of the problem.

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Como ya señalamos, una IA funcional y de calidad es la base esencial para una IA ética y legal. En este post resumimos los dos principales estándares para crear, evaluar y certificar tanto los procesos software para desarrollar sistemas de IA (ISO/IEC 5338) como para el propio producto software de IA (ISO/IEC 25059). Ingeniería de…

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New Business Agenda based on ISO 38507 and ISO 42001. Artificial Intelligence (AI) has gone from being a technological trend to becoming a strategic pillar in business decision-making. However, its rapid adoption has raised new questions about governance, ethics, control and corporate responsibility. In this context, the international standards ISO 38507 and ISO 42001 are emerging as key frameworks for managing AI...

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The importance of data quality in AI. AI data quality can be understood as the set of properties (form both technical and business viewpoints) that make data suitable for training, validating, and operating AI models in a reliable, robust, fair, and auditable manner. In AI, poor-quality data leads to poor results...

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