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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As we pointed out, a functional, high-quality AI is the essential foundation for an ethical and legal AI. In this post, we summarise the two main standards for creating, evaluating and certifying both the software processes for developing AI systems ISO/IEC 5338 and the AI software product itself (ISO/IEC 25029).

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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 in a safe and structured manner with their focus on AI governance and management.

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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

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In recent months, with the arrival of ISO/IEC 42001, hundreds of articles, posts and opinions have proliferated on "AI governance", "AI management systems" and "how to implement responsible AI". Problem: analysing many of them, most confuse governance with management, or even use both terms as synonyms, which they are not. This confuses the reader and creates noise in the industry.

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The rapid evolution of Artificial Intelligence has driven the creation of new standards to help organisations manage it securely, reliably and in compliance with European regulations. In this context, different standards are emerging which, although related, perform complementary functions in the AI governance and compliance ecosystem. On the one hand, we have ISO/IEC 42001:2023

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Although all new information technology presents risks, AI is perhaps one of the technologies that increases the magnitude of risks the most, both negative (due to its potential impact at the organisational and social level) and positive (due to the great opportunities it offers, for example, for decision support). With regard to traditional IT risks, it should be noted that these evolve more rapidly in AI

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Artificial Intelligence (AI) increasingly influences 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. The benefits of intelligent systems are extraordinary, but the risks they entail may be even greater, making good AI governance more necessary than ever.

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If your company is using tools such as GitHub Copilot or ChatGPT to support software development, it is time to go beyond efficiency and ask how risks, automated decisions, and the quality of the generated code are being managed. ISO/IEC 42001 not only helps you comply with future legal requirements, but also establishes good organisational practices for integrating AI

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In this decade, AI will be to businesses what Excel was: indispensable. But, like Excel, you have to know how to use it. Currently, the use of Artificial Intelligence (AI) in companies has grown exponentially. Predictive models, machine learning algorithms, generative AI, virtual assistants, and autonomous agents are transforming processes, products, and services. However, this growth has also given rise to a worrying phenomenon: Shadow AI.

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