America is adrift from a rise of “Fisters”, that is to say “America Fisters“.

An “Aryan fist” is used by white supremacists such as neo-Nazis globally and the Ku Klux Klan in the United States. For example, the right-wing terrorist mass murderer Anders Behring Breivik saluted with a raised fist in court in Oslo in 2012.

The return of the “America First” ideology—a nativist hate group of the 1800s, better known as the KKK—has abruptly created a federal leadership vacuum swarming with raised fists. The contemporary usage has once again raised concerns about its inherently nationalist rhetoric. Critics argue that the slogan implicitly positions some Americans as more authentically “American” than others, intentionally weaponizing the labels of African American, Asian American, Hispanic American, Muslim American… anything that would acknowledge both a race, religion or cultural heritage with an American identity. Framing of “America” having to be first, then labeling non-whites of America as always second, drives historical hierarchies to deny America embracing the truth of multicultural composition.

As their always divisive rhetoric gains ground like it’s 1836 again (eve of Andrew Jackson’s moral and economic collapse sending America into depression and then Civil War), we’re witnessing the same destructive patterns that periodically threaten American unity. Over a century after General Grant destroyed them on the battle field, and next destroyed them at the voting booth as President Grant, we can clearly see Trump is bringing the KKK back.

But there’s a modern counterforce hidden in plain sight, which should be familiar to most people today—Fred Rogers. Not just the digestible Daniel Tiger most remember spun out of his era of troubled times, but also the deeply principled American hero as community-builder detailed in Michael Long’s “Peaceful Neighbor.” Rogers wasn’t creating mere entertainment; he was using entertainment as a vessel to offer a clear superior outcome.

“Only people who take the time to see our work can begin to understand the depth of it.” This is the invitation of Peaceful Neighbor, to see and understand Rogers’s convictions and their expression through his program. Mister Rogers’ Neighborhood, it turns out, is far from sappy, sentimental, and shallow; it’s a sharp political response to a civil and political society poised to kill.

While the old white supremacist nativists exploit fear of the other and destroy anything diverting from their fictional vision of self, Rogers long ago demonstrated how to build genuine connection across necessary and fruitful differences.

In 1847, Hungarian physician Ignaz Semmelweis made a revolutionary yet simple observation: when doctors washed their hands between patients, mortality rates plummeted. Despite the clear evidence, his peers ridiculed his insistence on hand hygiene. It took decades for the medical community to accept what now seems obvious—that unexamined contaminants could have devastating consequences.

Today, we face a similar paradigm shift in artificial intelligence. Generative AI is transforming business operations, creating enormous potential for personalized service and productivity. However, as organizations embrace these systems, they face a critical truth: Generative AI is only as good as responsibility for the data it’s built on—though in a more nuanced way than one might expect.

Like compost nurturing an apple tree, or a library of autobiographies nurturing a historian, even “messy” data can yield valuable results when properly processed and combined with the right foundational models. The key lies not in obsessing over perfectly pristine inputs, but in understanding how to cultivate and transform our data responsibly.

Just as invisible pathogens could compromise patient health in Semmelweis’s era, hidden data quality issues can corrupt AI outputs, leading to outcomes that erode user trust and increase exposure to costly regulatory risks, known as in integrity breaches.

Inrupt’s security technologist Bruce Schneier has argued that accountability must be embedded into AI systems from the ground up. Without secure foundations and a clear chain of accountability, AI risks amplifying existing vulnerabilities and eroding public trust in technology. These insights echo the need for strong data hygiene practices as the backbone of trustworthy AI systems.

Why Data Hygiene Matters for Generative AI

High-quality AI relies on thoughtful data curation, yet data hygiene is often misunderstood. It’s not about achieving pristine, sanitized datasets—rather, like a well-maintained compost heap that transforms organic matter into rich soil, proper data hygiene is about creating the right conditions for AI to flourish. When data isn’t properly processed and validated, it becomes an Achilles’ heel, introducing biases and inaccuracies that compromise every decision an AI model makes. Schneier’s focus on “security by design” underscores the importance of treating data hygiene as a foundational element of AI development—not just a compliance checkbox.

While organizations bear much of the responsibility for maintaining clean and reliable data, empowering users to take control of 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’s Solid standards—data quality improves at its source.

This dual focus on organizational and individual accountability ensures that both enterprises and users contribute to cleaner, more transparent datasets. Schneier’s call for systems that prioritize user agency resonates strongly with this approach, aligning user empowerment with the broader goals of data hygiene in AI.

Navigating Regulatory Compliance with the DSA and DMA Standards

With European regulations like the Digital Services Act (DSA) and Digital Markets Act (DMA), expectations for AI data management have heightened. These regulations emphasize transparency, accountability, and user rights, aiming to prevent data misuse and improve oversight. To comply, 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 data remains secure, transparent, and traceable. By addressing these regulatory requirements proactively, businesses can not only avoid compliance issues but also position themselves as trusted custodians of user data.

Moving Forward with Responsible Data Practices

Generative AI has tremendous potential, but only when its data foundation is built on trust, integrity, and responsibility. Just as Semmelweis’s hand-washing protocols eventually became medical doctrine, proper data hygiene must become standard practice in AI development. Schneier’s insights remind us that proactive accountability—where security and transparency are integrated into the system itself—is critical for AI systems to thrive.

By adopting tools like Solid, organizations can establish a practical, user-centric approach to managing data responsibly. Now is the time for companies to implement data practices that are not only effective but also ethically grounded, setting a course for AI that respects individuals and upholds the highest standards of integrity.

The future of generative AI lies in its ability to enhance trust, accountability, and innovation simultaneously. As Bruce Schneier and others have emphasized, embedding security and transparency into the very fabric of AI systems is no longer optional—it’s imperative. Businesses 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 charge towards a more ethical AI landscape.

The stakes are high, but the rewards are even greater. By championing responsible data practices, organizations can harness the transformative power of generative AI while maintaining the trust of their users and the integrity of their operations. The time to act is now—building AI systems on a foundation of well-cultivated data is the key to unlocking AI’s full potential in a way that benefits everyone.

Originally posted on TechRadar.