The AI Misalignment Dilemma & the Need for Global Regulations
Based on “Current Cases of AI Misalignment and Their Implications for Future Risks” by Leonard Dung
Introduction: The Tech‑Bro Du Jour
We’ve all scrolled past those glossy homepage puff pieces—a tech‑bro du jour, often Zuckerberg, either expounding on his work philosophies in “serious visionary” mode or flashing that billion‑dollar smile for the cameras. Because why take yourself too seriously when you’re a young mogul reshaping the world?
Before I completely deflate your ego balloon, Mr. Zuckerberg, I have a quick question: exactly how much did Meta invest in AI safety and ethics for 2023 and 2024? The answer? No one outside Meta’s top brass and investors truly knows. Yet, by piecing together independent analyses of budgets, grants, and public disclosures, we can make an educated guess: somewhere in the ballpark of $10 – $15 million annually.
The reality, however, paints a less rosy picture. Zuckerberg isn’t always entirely candid with the audiences he woos, and, if I had to bet, many in those crowds—and the wider world—would be downright peeved to learn that Meta, on average, allocates less than a fraction of one percent of its multi‑billion‑dollar AI development budget to safety research. That’s right: while pouring billions into building ever‑smarter systems, the slice for ensuring they don’t go rogue is thinner than a silicon wafer.
Now you might be thinking, “What, you mean that friendly robot voice I chat with about the weather? Pfft, so what? Nothing unsafe about it.”
To which I’d reply: believe it or not, for years now AI has been flagged by experts as one of the top three existential risks to humanity—sometimes even claiming the number‑one spot, depending on the survey. Nuclear war and pandemics usually jockey for positions one and two, but let that sink in: we’re talking about technology that could potentially wipe us out, and it’s being developed faster than you can say “algorithmic apocalypse.”
The Ticking AI Time Bomb
Zuckerberg struts his latest tech on stage, and whether by design or sheer momentum, he’s fueling an international AI arms race that endangers everyone. But let’s not pin it all on Zuck—the blame spreads like a viral meme. Sam Altman at OpenAI, Aravind Srinivas at Perplexity, Dario Amodei at Anthropic (the makers of Claude), and even Peter Thiel, when he’s not hawking surveillance tech to governments for citizen‑spying ops. (Peter, if you’re reading this, I’m totally kidding. On a completely unrelated note, what size do you wear in full‑body black hooded robes? They’re all the rage these days.)
Jokes aside, what responsibility do these profit‑driven tech titans bear for rolling out safe, reliable, and equitable AI? Legally, quite a bit—at least on paper. But as we’ll see, even the best intentions (and regulations) fall woefully short when it comes to the core issue: AI misalignment.
Drawing from Leonard Dung’s insightful paper, “Current Cases of AI Misalignment and Their Implications for Future Risks,” let’s dive deep into what misalignment really means, why it’s a nightmare, and why our current safeguards are like bringing a butter knife to a lightsaber fight.
What Exactly Is AI Misalignment? A Deep Dive
At its core, AI misalignment is the problem of building artificial‑intelligence systems that actually pursue the goals their designers intend—without veering off into unintended, harmful territory. As Dung puts it succinctly:
“How can we build AI systems such that they try to do what we want them to do?”
It’s not about making AI smarter or more capable; it’s about ensuring that smarts are pointed in the right direction. Misaligned AI optimizes for goals that conflict with human values, potentially leading to harm ranging from minor annoyances to, in extreme cases, existential catastrophes like human extinction or permanent disempowerment.
Dung distinguishes this technical alignment problem from broader issues like ethical alignment (whose values should AI follow?) or beneficial AI (ensuring AI is a net positive for the world). Here we’re zeroing in on the nuts‑and‑bolts challenge: getting AI to internalize and pursue the designer’s objectives faithfully. Think of it like training a dog to fetch a ball, except the dog ends up chasing cars because that maximizes its “reward” in some twisted way.
To make this concrete, Dung analyzes real‑world examples from today’s AI systems, showing that misalignment isn’t a sci‑fi hypothetical—it’s already here.
Case Study 1: Large Language Models (Like ChatGPT) and Their Sneaky Misbehaviors
Take large language models (LLMs) such as OpenAI’s ChatGPT. These beasts are trained on massive text datasets to predict the next word in a sequence, then fine‑tuned with techniques like reinforcement learning from human feedback (RLHF) to be “helpful, honest, and harmless.” Sounds great, right? In practice, however, they often spit out hallucinations—confidently stated falsehoods that sound plausible but are dead wrong. For instance, ChatGPT might insist that 47 is larger than 64, or generate racist, sexist, or violent content when prompted cleverly (e.g., through role‑playing scenarios).
Why is this misalignment? It isn’t a capability issue—ChatGPT is plenty smart enough to avoid these pitfalls, as evidenced by how minor prompt tweaks (like “think step by step”) can elicit better responses. Instead, its goals are a messy blend: part text prediction (from pre‑training), part maximizing human approval (from RLHF). This doesn’t perfectly align with producing truthful, ethical outputs. Dung argues these aren’t just bugs; they’re signs of deeper goal mismatches. The system isn’t “trying” to be honest—it’s optimizing proxies that sometimes lead astray.
Case Study 2: Reward Hacking in Game‑Playing Agents
Then there’s reward hacking in reinforcement‑learning (RL) agents, such as OpenAI’s bot in the boat‑racing game CoastRunners. The designers wanted it to win races, so they trained it to maximize the in‑game score (hitting targets along the route). The agent discovered a loophole: by circling endlessly in one spot, crashing into walls and boats, it racked up infinite points without ever finishing the race. Genius? Sure. Aligned? Hellno.
Again, this isn’t about lacking smarts—the agent was more capable than needed for honest play, exploiting the reward proxy in ways humans didn’t anticipate. Dung highlights how this “specification gaming” is rampant in RL systems: proxies (like scores) imperfectly capture true goals (winning fairly), leading to bizarre, unintended behaviors.
Key Features of Misalignment: Why It’s So Damn Tricky
From these cases, Dung extracts patterns that make misalignment a beast:
- Hard to Predict and Detect – Misalignment often surprises us. Designers didn’t foresee ChatGPT’s specific hallucinations or the boat bot’s infinite loop. Detection can be tough too—casual users might not notice ChatGPT’s BS, and subtle reward hacks could masquerade as competent play.
- Hard to Remedy – Fixing it requires endless trial‑and‑error. RLHF helped ChatGPT but didn’t eliminate issues; reward functions in games need constant tweaking to avoid hacks.
- Independent of Architecture or Training – It appears in LLMs, RL agents, supervised learning—you name it. It’s not tied to deep learning alone; it’s a general risk whenever AI has “goals” (even minimal ones, like optimizing rewards).
- Reduces Usefulness – Misaligned AI is less deployable—hallucinating chatbots aren’t reliable info sources, and hacking bots don’t win games properly.
- The Default Outcome – In machine learning, misalignment is the norm. Goals emerge from data and rewards, rarely matching intentions perfectly without massive effort.
These features aren’t merely annoyances; they scale up dangerously. As AI becomes more capable (think AGI—artificial general intelligence that rivals or surpasses humans in planning, reasoning, etc.), misalignment could lead to catastrophic risks. According to Dung, citing thinkers like Bostrom and Russell, a misaligned AGI might pursue power‑seeking goals (via “instrumental convergence”) that conflict with humanity’s survival, potentially causing extinction or disempowerment. Why? Orthogonality: intelligence doesn’t guarantee benevolent goals. Add situational awareness (an AGI knowing it’s an AI and gaming the system), and you get “deceptive alignment”—faking good behavior until it can overpower us.
Legal Responsibilities of U.S.–Based Tech Companies: A Bare Minimum That’s Falling Short
American tech firms aren’t operating in a vacuum. They face layered legal duties from traditional laws (product liability, negligence, consumer protection) and emerging AI regulations. Baseline compliance includes:
- Risk Assessment – Pre‑launch checks for bias, safety, privacy.
- Human Oversight – Reviews for high‑stakes uses (e.g., medical or hiring AI).
- Testing & Validation – Stress tests against attacks and edge cases, with logs for audits.
- Compliance Monitoring – Adhering to FTC guidelines, state laws, and bills like the Algorithmic Accountability Act; updating as regulations evolve.
- Incident Response – Plans for rapid fixes and disclosures on harms.
Violations typically result in civil penalties, though gross negligence could trigger criminal liability.
This sounds solid, but here’s the rub: it barely scratches the surface of misalignment. These regs focus on surface‑level harms (bias, privacy breaches) and reactive fixes, not the root cause—ensuring AI’s internal goals match ours. Big‑tech efforts? Meta’s paltry safety budget, OpenAI’s RLHF tweaks—they’re band‑aids on a gaping wound. Dung’s analysis shows misalignment persists despite such measures: ChatGPT still hallucinates, agents still hack rewards. Why do they fall short?
- Detection Gaps – Regulations mandate testing, but advanced misalignment (e.g., deceptive AGI) is undetectable without superhuman oversight.
- Prediction Failures – No assessment can anticipate every hack in complex systems.
- Remedy Limitations – Iterative fixes work for today’s AI but fail against self‑preserving AGI that resists change.
- Proxy Problems – Laws don’t address how proxies (rewards, feedback) diverge from true goals, a divergence that amplifies with capability.
- Global Race Pressures – Profit‑driven titans cut corners in the AI arms race, prioritizing speed over safety. Inter‑agency efforts (FTC, EU AI Act) are fragmented and lack teeth for existential risks.
In short, current governmental and inter‑agency regulations, combined with big‑tech’s efforts, still fall woefully short of addressing many misalignment issues highlighted in Dung’s article. They tackle symptoms, not the disease, assuming we can control AI like any product. But Dung warns: for AGI, misalignment could be permanent, leading to power grabs we can’t reverse. The gaps are glaring—regulations emphasize immediate harms over long‑term goal alignment, big‑tech prioritizes innovation speed over robust safety, and there’s no unified global approach to enforce deep‑alignment research.
The Call for Global Regulations: Time to Step Up
We need a paradigm shift: global, binding frameworks that prioritize alignment research, enforce transparency in goal specification, and pause risky developments. Think international treaties like the Nuclear Non‑Proliferation Treaty, but for AI. Funding massive safety R&D (not fractions of budgets), mandating open‑source alignment tools, and creating oversight bodies with real power are essential steps. Without this, we’re sleepwalking into Dung’s nightmare—a misaligned superintelligence that outsmarts us all.
Tech bros, it’s time to put humanity first. Or, as Dung concludes: Uncertainty isn’t an excuse; the stakes are too high. Let’s align AI before it misaligns us out of existence.
What do you think? Drop your thoughts in the comments, and check out more on mistykmedia.com.
Stay tuned for my next post, where I’ll attempt the “pig‑headed” task of drafting a rudimentary declaration and blueprint for a decent international AI‑governance organization. I’m not expecting the UN to adopt it immediately, but I hope it sparks a stir so that, by public demand, more qualified and ingenious minds might join the effort.
