When Politics Dictates AI: Why Trust, Safety, and Data Privacy Must Stay Non-Negotiable

We’re hearing a familiar refrain again: make it neutral. Strip the bias. Clean it up. But whose version of “neutral” are we embedding in the machines that now mediate how we see the world?

This week, the Trump administration rolled out an executive order demanding that AI systems used by U.S. federal agencies remain free of “woke bias.” At first glance, it sounds like a call for balance. In practice, it’s a red flag for trust and safety professionals, privacy advocates, and builders of ethical AI everywhere.

We’ve long known that algorithms can entrench biases. We also know that addressing this isn’t about ideology – it’s about responsibility. When AI systems are directed to actively avoid recognising injustice or diversity, we’re not making them neutral. We’re making them complicit in erasure.

And when governments – particularly ones with global reach – start dictating the political content of code, we all need to sit up straight and tighten the data security bolts. Because the impact doesn’t stop at the borders of the U.S. federal government.

It echoes. It infects. And if we’re not careful, it corrodes the fragile trust we’ve spent decades building in digital systems.

What’s Happening and Why It Matters

Under the new order, U.S. federal agencies are being told they must not use AI systems that exhibit “ideological bias.” The targets? Terms like DEI, critical race theory, and other frameworks that examine power, identity, and systemic inequality. Any AI that generates or supports such content will be disqualified from federal procurement.

But these directives don’t exist in a vacuum. The U.S. is one of the largest global buyers of technology. What’s banned in Washington today may soon be quietly deprecated in Silicon Valley’s foundation models – especially those whose revenue depends on enterprise and government clients.

The problem? When foundational models are trained or fine-tuned to downplay issues like race, gender, or abuse (under the guise of “neutrality”), they don’t just avoid controversy. They strip out context. And in the process, they fail to recognise risk – especially when it comes to data privacy, online harm, and targeted abuse.

If your AI refuses to name racism or coercion, how can it protect the survivor reporting a deepfake or digital stalking? If it avoids discussions of gender identity, how can it flag pattern-based harms in gendered abuse?

“Bias Removal” ≠ Better AI

Let’s be clear: all models are trained on something. The moment you choose a data source, a label, or a category – you’re making decisions that affect outcomes. There is no “unbiased AI.” There is only transparent AI, and accountable AI.

And there is AI that takes harm seriously.

“Bias removal,” when driven by politics instead of principle, doesn’t lead to safer systems. It leads to:

• Sanitised models that refuse to acknowledge abuse
• Chatbots that parrot disinformation under the guise of neutrality
• Surveillance systems that over-police some groups while ignoring others
• Gag orders on datasets that reflect lived experience

We’ve been down this path before – with discriminatory sentencing algorithms, with racist facial recognition, and with moderation systems that silence marginalised voices while leaving threats intact. Are we really going to pretend again that pretending the problem doesn’t exist… makes it go away?

Trust Is Built on Transparency and Consent

From a data privacy and security lens, these developments are especially troubling. The less transparent a model is – about what it knows, how it was trained, and how it’s being shaped post-release – the more difficult it is to audit, govern, or trust.

And trust isn’t just a “nice to have.” It’s the backbone of every successful AI deployment.

Whether you’re a small business owner using AI to sort customer queries, or a government agency deploying a chatbot for social services, you need to know:

• What data is being used?
• Where did it come from?
• What was filtered out – and why?
• Can I see how this model reached its answer?
• Is it safe for everyone to use – including the vulnerable and marginalised?

When political actors start dictating what’s acceptable for a model to say or know, those questions become harder to answer. And that opens the door not just to bias – but to backdoors, data abuse, and downstream harms we can’t easily reverse.

What Responsible Builders Must Do Now

As founders, engineers, policymakers, and users, we have a choice. We don’t have to play by broken rules that reward obfuscation and punishment over clarity and protection.

Here’s how we can hold the line:

1. Document everything – Show your training data sources, disclose alignment methods, and explain your moderation philosophy in plain language.
2. Design for dignity – Prioritise user control, revocable consent, and zero-knowledge architectures that don’t collect more than they need.
3. Refuse false neutrality – If your AI refuses to recognise injustice, it isn’t neutral. It’s just ignoring the data that makes people unsafe.
4. Push for global standards – Don’t let one country’s politics shape global norms. We need international frameworks for transparency, consent, and AI safety.
5. Speak up – When public policy veers toward erasure, it’s on us to call it out – not for the sake of “politics,” but for the safety of everyone who lives and works online.

What Responsible AI Users Can Do

You don’t need to be an engineer or policymaker to shape the future of AI. Every prompt you write, every model you use, and every question you ask contributes to the culture around these systems.

If we want AI to serve people – not agendas – we need to engage as informed, critical users. That means asking more of the tools we rely on, and more of ourselves.

Here’s how we can hold the line from the user side:

• Ask questions. Don’t take outputs at face value. Who trained this model? What data is it drawing from? What topics does it avoid?
• Demand transparency. Choose tools that tell you what’s under the hood. Prioritise platforms that offer audit logs, consent options, and clear terms of use.
• Spot the gaps. If your AI avoids topics like abuse, race, or gender – or gives vague, incomplete answers – flag it. Silence is a signal.
• Give feedback. When platforms offer reporting or feedback tools, use them. Your pushback helps improve systems for everyone.
• Champion ethics in your networks. Whether you’re a teacher, founder, parent, or community leader – speak up about responsible use. Normalise consent, challenge censorship, and support alternatives that prioritise safety and dignity.

The Road Ahead

This isn’t about political labels. It’s about digital integrity. The AI systems we build today will shape how future generations understand the world – and each other. Sanitised, censored systems might seem safer to deploy, but they are far more dangerous to trust.

If we care about privacy, if we care about safety, if we care about keeping people – not just profits – at the heart of the AI conversation, then we must build systems that are capable of acknowledging harm, bias, and risk.

And that means refusing to flatten the truth in the name of political convenience.

Because true safety doesn’t come from silencing discomfort. It comes from facing it – with clarity, compassion, and courage.

About the Author:

Kim Chandler McDonald is the Co-Founder and CEO of 3 Steps Data, driving data/digital governance solutions.
She is the Global VP of CyAN, an award-winning author, storyteller, and advocate for cybersecurity, digital sovereignty, compliance, governance, and end-user empowerment.