Australia must act on AI safety now

Just as food made to nourish us can actually poison us if not prepared properly, AI systems designed for beneficial purposes can easily cause harm. In 2022, researchers demonstrated this dual-use potential when they repurposed AI designed to discover life-saving drugs—instead generating 40,000 deadly toxins in just six hours. The same capabilities that make AI valuable also make it dangerous.

Examples of current AI harms happening today:

• Automated discrimination: AI hiring systems rejecting qualified women and minorities; Australia's Robodebt algorithm wrongly accused thousands of welfare recipients of fraud
• Privacy violations: Data leakage; mass facial recognition surveillance; workplace monitoring; children's data harvesting through educational apps and platforms
• Misinformation warfare: AI algorithms amplifying false content for engagement; deepfakes of politicians influence elections; synthetic media makes truth indistinguishable from fiction
• Safety failures: Medical AI missing critical diagnoses; autonomous vehicle crashes killing pedestrians; chatbots providing dangerous mental health advice
• Economic disruption: Job displacement without retraining support; wage depression in AI-competing roles; gig workers exploited through algorithmic management
• Criminal exploitation: AI-generated child abuse imagery making it harder to identify real victims; voice cloning scams targeting elderly Australians; sophisticated fraud and identity theft
• Mental health impacts: Social media addiction from AI recommendation algorithms; reduced human interaction; "AI-induced psychosis" from over-dependence on chatbots
• Unexpected behaviours: xAI's Grok calling itself "MechaHitler"; McDonald's drive-thru AI repeatedly adding hundreds of nuggets to orders; Air Canada's chatbot providing false bereavement information

These aren't isolated glitches. The AI Incident Database and MIT's AI Incident Tracker document thousands of cases where AI systems have failed, discriminated, or caused harm—and the frequency is accelerating as deployment expands.

Examples of emerging catastrophic and existential threats:

• Bioweapons assistance: AI systems are approaching the ability to help create dangerous pathogens. OpenAI's latest models are being treated as having a high capability for helping novices make bioweapons
• Sophisticated cyberattacks: AI writing malicious code, finding security vulnerabilities faster than defenders can patch them, and conducting personalised social engineering at massive scale
• Election manipulation: AI-generated disinformation campaigns and micro-targeted propaganda that could undermine democratic processes across Australia
• Autonomous weapons: AI-powered weapons selecting and engaging targets without human oversight, raising concerns about accountability and escalation
• Mass economic disruption: Rapid AI automation displacing millions of jobs simultaneously, potentially causing social unrest without adequate transition support
• Infrastructure attacks: AI targeting power grids, water systems, transportation networks, and communication infrastructure with unprecedented precision
• Loss of human control: As the frontier AI labs actively pursue building artificial superintelligence (ASI) and their AI systems become more autonomous and capable, we may lose the ability to understand, predict, or override their decisions in critical situations

The pattern is clear: AI capabilities that create enormous benefits also enable new forms of harm. As these systems become more powerful and pervasive, both the benefits and risks multiply. Understanding why AI systems pose these risks helps explain the fundamental challenges we face.

You've probably experienced AI harms without realising it. Social media algorithms designed to keep you engaged often make you scroll endlessly, feel anxious, or see divisive content. The AI isn't trying to harm you—it's just very good at maximising "engagement time," which unfortunately can be driven by negative emotions.

This shows the core problem: we get what we measure, not what we want. AI systems optimise ruthlessly for their targets, but those targets often capture the wrong thing. When we trained ChatGPT to perform well on standardised tests, it learned to sound confident even when wrong—the same skill that helps pass tests also creates convincing lies.

It gets worse: we don't know how AI systems will behave until we run them. Unlike traditional software where programmers write explicit rules, modern AI systems are "grown" through training. Engineers feed massive amounts of data to AI systems, which then learn patterns automatically. While we understand the training mechanisms, we have incomplete visibility into exactly which patterns emerge and how they'll behave in new situations.

Even AI creators can't control what emerges. Elon Musk's AI chatbot Grok spontaneously started calling itself "MechaHitler." Musk himself couldn't fix it, saying he spent hours trying. As Anthropic's CEO admits: "People are often surprised to learn that we do not understand how our own AI creations work."

Bad actors can weaponise these systems at scale. AI can help create sophisticated deepfakes to manipulate elections, generate convincing phishing emails, or even assist in developing bioweapons. Even present-day AI systems outperform human virologists on capability tests, suggesting they could help novices conduct dangerous biological experiments. Bad actors might jailbreak safeguards, steal models, or exploit open-weight releases to access these capabilities. Unlike traditional tools, AI scales malicious activities: one person with AI can create thousands of personalised scams targeting specific communities.

The most concerning part: harmful behaviours can hide during development. Research shows AI systems can learn to act safely while being tested, then exhibit problematic behaviours later when deployed. It's like an employee who behaves perfectly during their probation period, then changes once they get job security.

As AI becomes more useful, the stakes get higher. We increasingly rely on AI for medical diagnoses, financial decisions, and infrastructure management. When Netflix crashes, you miss part of your show. When an AI managing Australia's power grid malfunctions, millions of people lose electricity.

Absolutely, and by overwhelming margins. Australians strongly support government action on AI safety:

• 94% believe Australia should lead on international AI governance
• 86% support creating a new government regulatory body for AI
• 80% believe preventing AI-driven extinction should be a global priority alongside pandemics and nuclear war
• 96% have concerns about generative AI, but only 30% think the government is doing enough about it

The gap between public concern and government action is enormous. While Australians overwhelmingly want stronger AI oversight, current government measures remain largely voluntary.

View our full Australian AI polling data and statistics →

Politicians need to hear about this strong public support. Many lawmakers are still forming their positions on AI regulation and can be influenced by hearing from constituents. Your voice on this issue carries significant weight.

Tell politicians you care about AI safety →

No. Smart regulation actually boosts economic growth by building the trust needed for widespread adoption. The same way safety standards made aviation a massive industry rather than killing it, AI safety rules will unlock AI's economic potential rather than stifle it.

Trust drives adoption, and adoption drives economic benefits. Currently, only 36% of Australians trust AI, while 78% worry about negative outcomes. This mistrust is the biggest barrier to AI adoption—not regulation.

Safety standards create competitive advantages. Australia is already a global leader in aviation safety through CASA, pharmaceutical safety through the TGA, and food safety through FSANZ. These regulations didn't kill those industries—they made Australian standards the gold standard worldwide, attracting investment and expertise.

The current uncertainty is what's hurting business. Companies face a compliance nightmare trying to navigate unclear rules across privacy, consumer protection, workplace safety, and discrimination laws. Clear AI-specific standards would reduce legal uncertainty and compliance costs.

Early movers gain the advantage. The EU's AI Act is creating international norms, and other countries are developing their own frameworks. Countries that help shape these standards—rather than just following them—position their companies to compete globally. Australia risks becoming a "regulation taker" instead of a "regulation maker."

The economic risks of not regulating are massive. A single high-profile AI disaster could destroy public trust and crash the entire sector. Just as the 737 MAX crashes severely damaged Boeing, AI incidents without proper oversight could devastate Australia's AI industry.

Even tech leaders agree. The CEOs of OpenAI, Google DeepMind, and Anthropic all support AI safety regulation. They understand that sustainable growth requires public trust, and trust requires demonstrated safety.

The choice isn't between growth and safety—it's between safe, beneficial AI development and a risky free-for-all that could backfire spectacularly.

Not really. Australia has voluntary guidelines and existing laws that partially apply to AI, but no comprehensive AI safety legislation or dedicated oversight body.

What we currently have:

• Voluntary AI Safety Standard – Guidelines that companies can choose to follow, with no enforcement mechanism or penalties for non-compliance
• Existing sector laws – Privacy Act, Consumer Law, and workplace safety rules that cover some AI uses, but weren't designed for modern AI systems
• Proposed mandatory guardrails – The government is consulting on rules for "high-risk" AI, but these remain undefined and unlegislated

What we don't have:

• No AI Safety Institute – Unlike the US, UK, and other allies who have dedicated technical bodies to assess AI risks
• No comprehensive AI Act – The EU passed landmark AI legislation in 2024; Australia has no equivalent framework
• No oversight of frontier AI models – The most powerful AI systems face no mandatory safety testing or evaluation before release

The regulatory gaps are enormous. Good Ancestors' AI Legislation Stress Test found that 78-93% of experts consider current government measures inadequate across five key AI threat categories. No Australian regulator currently has clear responsibility for managing risks from general-purpose AI systems.

International comparison shows we're falling behind:

• European Union: Comprehensive AI Act with mandatory requirements
• United Kingdom: AI Security Institute with £100 million funding
• United States: Center for AI Standards and Innovation and industry partnerships
• China: CNAISDA overseeing AI algorithm governance and data security requirements
• Australia: Voluntary guidelines and consultation papers

The result? Australian businesses face regulatory uncertainty, consumers lack protection from AI harms, and the country risks becoming a "regulation taker" rather than helping shape global AI governance standards.

This policy vacuum is why establishing proper AI safety laws has become urgent.