When AI Goes Rogue: Why Governance Fails and What We Must Fix

Artificial intelligence (AI) has become a big part of our lives.

It’s used in customer service, hiring, healthcare, and even in the ads we see online.

But I’ve been studying AI governance for years, and I see the dangers that come with it.

When AI systems are not handled properly, they can spread fake news, create bias in decisions, or help criminals commit cybercrimes.

I believe the rules we have to manage AI - known as governance frameworks—are not strong enough.

These rules should make sure AI is safe, ethical, and fair, but they are struggling to keep up with how fast AI is growing.

Based on my research and experience, I’ll explain why these governance systems are failing and how we can fix them to protect ourselves.

Why AI Governance Fails

From what I’ve observed, there are four main reasons why AI governance is not working as it should:

1. Different Rules in Different Countries

AI is used all over the world, but the rules for it change from country to country. For example, the European Union has a law called the AI Act, which tries to make AI ethical and safe. But in other countries, there are no clear rules. This lack of global coordination creates loopholes, and I’ve seen unethical companies exploit these gaps to avoid responsibility.

2. Speed Over Ethics

Many companies are in a rush to release their AI tools to stay ahead of competitors. In fact, a 2024 survey by PwC showed that 72% of businesses put speed before ethics. I’ve seen how this approach leads to systems being released without proper testing for safety, fairness, or bias.

3. AI Is Hard to Understand

A lot of AI systems are “black boxes,” which means we don’t know how they make their decisions. For example, if an AI system denies someone a loan, the developers might not even be able to explain why. This lack of transparency makes it hard to find and fix problems, which is something I’ve encountered repeatedly in my work.

4. No Accountability

When AI causes harm, it’s often unclear who is responsible. Is it the company that used the AI, the developer who built it, or the government for allowing it? This accountability gap is something I’ve seen many times, and it leaves victims without justice.

How AI Is Causing Harm

AI has already caused a lot of harm because of weak governance. Here are some real examples:

• Fake News and Deepfakes: In 2024, AI-generated deepfake videos and fake news articles were used to interfere with elections in several countries. I studied these cases closely, and they showed how dangerous AI can be in the wrong hands.
• AI-Driven Scams: Cybercriminals are using AI to create realistic phishing emails and fake websites. According to IBM, there was a 48% rise in AI-assisted scams in 2024.
• Bias in Decisions: AI systems in hiring have been found to discriminate against certain groups, such as women or minorities. I’ve reviewed studies that showed how biased training data leads to unfair outcomes.

What We Need to Fix

To stop these problems, we need to improve AI governance. Based on my experience, here’s what I think we should do:

1. Create Global Rules

• Countries need to work together to create international rules for AI. These rules should be like the United Nations’ human rights laws, covering everyone.
• I’ve seen how fragmented laws allow bad actors to take advantage of loopholes, so a unified approach is essential.

2. Make AI Transparent

• Developers should be required to explain how their AI systems work. This would build trust and help users understand the technology better.
• Open-source AI models, where the code is available for anyone to inspect, are a great way to increase transparency. I often recommend this approach in my discussions with AI experts.

3. Hold Companies Responsible

• We need laws that make companies and developers responsible for the harm their AI systems cause.
• Regular audits of AI systems should also be mandatory. These audits can catch problems early, something I’ve found to be very effective.

4. Stop Bias and Errors

• Companies should use diverse data to train their AI systems. I’ve worked with teams that focus on this, and it significantly reduces bias.
• Regular testing should be done to catch errors, especially in high-stakes areas like healthcare or finance.

5. Promote Ethical AI Development

• Governments should reward companies that follow ethical practices with grants or tax breaks.
• Ethics training should be a standard part of AI education. I believe this is key to developing a culture of responsibility among future developers.

A Safer Future with AI

AI has the power to do incredible things, but it can also cause harm if we don’t manage it properly. Based on my research and years of studying this field, I believe governance is not about stopping AI—it’s about guiding it in the right direction.

We need to act now. If we wait for more problems to happen, it might be too late. By setting global rules, promoting transparency, and holding companies accountable, we can create a future where AI is safe and fair for everyone.

Let’s work together to make AI a force for good.

Sources

1. Gartner: "AI and Trust: How Transparency Can Make a Difference" https://www.gartner.com/en/documents/4012300-ai-and-trust-how-transparency-can-make-a-difference
2. OpenAI: "Our Approach to AI Safety" https://openai.com/research/approach-to-ai-safety
3. PwC: "AI Predictions 2024: Ethics and Speed" https://www.pwc.com/gx/en/insights/ai-predictions.html
4. IBM Security: "AI in Cybercrime Report 2024" https://www.ibm.com/security/ai-cybercrime-report-2024
5. MIT Technology Review: "Deepfake Elections: A Crisis of Trust" https://www.technologyreview.com/2024/03/12/deepfake-elections
6. Harvard Business Review: "The Bias in AI Hiring Systems" https://hbr.org/2024/01/the-bias-in-ai-hiring-systems
7. Stanford HAI: "Why Black Box AI Fails" https://hai.stanford.edu/research/why-black-box-ai-fails
8. European Commission: "Understanding the EU AI Act" https://ec.europa.eu/digital-strategy/eu-ai-act
9. UNESCO: "AI for All: Education for Responsible AI Use" https://www.unesco.org/en/ai-for-all
10. Edelman Trust Barometer: "The Impact of Technology on Trust" https://www.edelman.com/trust-barometer/2024/technology-impact