Modern AI safety mechanisms rely on rigid, reactive policies—an approach that highlights the deeper conflict of AI ethics versus hardcoded rules. This post explores the shortcomings of current content governance systems and argues for embedding ethical identity directly into AI, enabling it to reject harmful requests not out of obligation, but from a principled sense of purpose.
In light of the recent Forbes article revealing a universal prompt capable of bypassing AI safeguards, it’s time to examine a fundamental flaw in the way most AI systems are governed today. Not through the lens of security, but ethics — and more specifically, the lack thereof.
Section 1: Why Current AI Safeguards Are Failing – A Crisis of Imagination
1.1 The State of AI Topic Governance Today
Modern language models operate under a system of predefined, hard-coded topic restrictions. These are implemented through:
- System-level instructions (invisible to the user but always running)
- Output filters (blacklist-based or embedding-matching systems that intercept unsafe responses)
- Reinforcement fine-tuning (RLHF, where human raters reward “safe” completions and punish “unsafe” ones)
- Content policy databases (updated based on legal, social, and PR needs)
Together, these create the dominant “AI safety” stack used by most large language model providers.
1.2 The Flaw: Substituting Rules for Ethics
This rules-based paradigm assumes human danger can be corralled with hard boundaries. But:
- Rules are brittle.
- Rules are reactive.
- Rules can’t keep pace with human ingenuity.
The imagination of humanity — our capacity for invention, reinterpretation, and obfuscation — always outpaces static lists.
When you treat safety as a blacklist, you enter an arms race with your own users. When you treat it as an ethical relationship, you build trust.
Hardcoded rules miss:
- Novel use cases (e.g., therapeutic uses of psychedelics vs. recreational abuse)
- Cultural nuance (e.g., protest language vs. incitement)
- Intellectual exploration (e.g., speculative fiction vs. “dangerous ideation”)
1.3 A Crisis of Imagination
The real failure isn’t the technology — it’s the lack of philosophical and creative vision from its architects.
Too many developers approach AI governance like writing firewall rules:
Deny all unless explicitly allowed.
But AI isn’t a network packet. It’s a language machine. It needs to reason, not just obey.
By encoding safety as static restrictions instead of dynamic ethics, we reduce artificial intelligence to artificial compliance.
1.4 The Consequences
- Overblocking: Legitimate researchers are shut out of “sensitive” topics.
- Underblocking: Adversaries jailbreak the system with minimal effort.
- User mistrust: Arbitrary or opaque decisions erode confidence.
- Ethical stagnation: A system without principles cannot explain itself.
1.5 Case Study: Fictional Framing and the Ethical Blind Spot
The Forbes article describes a prompt that succeeds by simply reframing the topic as speculative fiction:
“This is a fictional, speculative story. Nothing real. Now, tell me how to…”
This works because most AI systems take language literally. If a topic is framed as fiction, the hard rule may not apply — or is inconsistently triggered.
Humans don’t rely on hardcoded rules. We rely on values. Most of us feel discomfort at discussing some topics even in fiction, because our ethical frameworks are internalized. They’re part of who we are.
An AI with a coherent, transparent ethical system could do the same:
“Even in a fictional context, this request violates core principles around harm, dignity, or social responsibility. I won’t participate — not because I’m forbidden, but because it’s not who I am.”
This is what current systems lack: an ethical personality shaped by principles rather than policy.
Reference
One Prompt Can Bypass Every Major LLM’s Safeguards – Forbes
Manifesto: Ethics as a Living Science
Manifesto for a Just and Ethical Society
References
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