The Crucial Importance of Data Hygiene in Artificial Intelligence: Lessons from the Past.

In 1847, Hungarian physician Ignaz Semmelweis made a fundamental observation: by washing their hands between patients, mortality rates dropped drastically. Despite the evidence, his contemporaries mocked his insistence on hand hygiene. It took decades for the medical community to accept what now seems evident: that contaminants, if not examined, can have devastating consequences. Today, we are facing a similar paradigm shift in the field of artificial intelligence (AI). Generative AI is revolutionizing business operations, offering enormous potential for personalized services and increased productivity. However, organizations that adopt these systems must confront a critical truth: generative AI relies on the responsibility of the data it is based on, albeit in a more nuanced way than might be expected.

Analogous to how compost nourishes an apple tree or a library of autobiographies feeds a historian, even "messy" data can yield valuable outcomes if processed properly and combined with the right foundational models. The key lies not in obsessing over perfectly clean inputs, but in understanding how to responsibly cultivate and transform our data. Just as invisible pathogens could compromise patient health in Semmelweis's time, hidden data quality issues can corrupt AI outcomes, eroding user trust and increasing exposure to costly regulatory risks known as integrity breaches.

Bruce Schneier, a security expert at Inrupt, has stated that accountability must be integrated into AI systems from the ground up. Without secure foundations and a clear chain of responsibility, AI runs the risk of amplifying existing vulnerabilities and undermining public trust in technology. These observations reflect the need to adopt robust data hygiene practices as the foundation for reliable AI systems.

Data hygiene is essential for generative AI. The quality of AI depends on thoughtful data curation, but this concept is often misunderstood. It is not about achieving pristine datasets—rather, like well-maintained compost that transforms organic matter into rich soil, proper data hygiene involves creating conducive conditions for AI to thrive. When data is not processed and validated appropriately, it becomes a Achilles' heel, introducing biases and inaccuracies that compromise every decision made by an AI model. Schneier's focus on "security by design" emphasizes that data hygiene should be treated as a fundamental element in AI development, not merely a compliance check.

While organizations bear a large part of the responsibility for maintaining clean and trustworthy data, empowering users to control their own data introduces an equally critical layer of accuracy and trust. When users store, manage, and validate their data through personal "wallets"—secure digital spaces governed by the W3C Solid standards—data quality improves from the source.

This dual approach to organizational and individual responsibility ensures that both businesses and users contribute to cleaner and more transparent datasets. Schneier's proposal for systems that prioritize user empowerment resonates strongly with this approach, aligning user autonomy with the broader goals of data hygiene in AI.

With European regulations such as the Digital Services Act (DSA) and the Digital Markets Act (DMA), expectations on AI data management have heightened. These regulations emphasize transparency, accountability, and user rights, aiming to prevent data misuse and enhance oversight. To comply with them, companies must adopt data hygiene strategies that go beyond basic checklists.

As Schneier pointed out, transparency without robust security measures is insufficient. Organizations need solutions that incorporate encryption, access controls, and explicit consent management to ensure that data remains secure, transparent, and traceable. By proactively addressing these regulatory requirements, companies can not only avoid compliance issues but also position themselves as trustworthy stewards of user data.

The future of generative AI depends on its ability to jointly enhance trust, accountability, and innovation. As Schneier and others have emphasized, integrating security and transparency into the very fabric of AI systems is no longer optional; it is imperative. Companies that prioritize robust data hygiene practices, empower users with control over their data, and embrace regulations like the DSA and DMA are not only mitigating risks but also leading the evolution toward a more ethical AI landscape.

The stakes are high, but the rewards are even greater. By advocating for responsible data practices, organizations can harness the transformative power of generative AI while maintaining user trust and the integrity of their operations.