Understanding AI Risks: From Bias to Existential Threats

As artificial intelligence systems continue to advance at a remarkable pace, understanding the associated risks becomes increasingly important. This article provides an overview of key AI risks—from well-documented concerns like algorithmic bias to more speculative existential threats—drawing insights from current research and real-world examples.1

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The Spectrum of AI Risks

AI risks exist on a spectrum, varying in both immediacy and severity. Some are already observable in deployed systems, while others remain theoretical but potentially catastrophic.

Algorithmic Bias and Fairness

AI systems often perpetuate or amplify existing societal biases through their training data and design choices. Research has revealed nationality bias in job recommendations from models like ChatGPT and LLaMA, while other studies have documented gender bias affecting career advice provided to women.23 These biases arise from multiple factors, including demographic disparities in internet access—as of 2016, 78% of North America had internet access compared to just 20% in Sub-Saharan Africa, creating significant representation gaps in web-scraped training data.

These imbalances emerge throughout the machine learning lifecycle—from data collection to deployment—and can lead to discriminatory outcomes in critical areas like hiring, lending, and healthcare.

Privacy and Data Security

The development of increasingly powerful AI models comes with significant privacy implications. Tech giants have been criticized for harvesting vast amounts of data without clear consent, driven by established scaling laws showing that model performance improves predictably as both data and model size increase.4 Companies like Clearview AI have scraped billions of images from social media to build facial recognition tools, raising serious privacy concerns about data collection practices and consent.5

Automation and Job Displacement

The labor market impact of AI is evolving rapidly. While automation in the 2010s primarily threatened physical labor jobs through advances in computer vision and robotics, the 2020s pose risks to knowledge workers. Accountants, communication specialists, call center agents, translators, and software developers are now among the occupations most vulnerable to AI-driven automation. Recent surveys indicate that 43% of companies report job cuts due to AI automation, with 25% planning future reductions.6

This shift raises important economic and social questions about wealth distribution when automation benefits primarily accrue to executives and shareholders rather than being broadly shared across society.

Misinformation and Disinformation

AI systems now pose serious risks to information integrity. Generative AI can produce convincing human-like content at scale, making it difficult to distinguish from authentic human communication. Users often exhibit automation bias, placing excessive trust in AI-generated information even when it's incorrect. Additionally, malicious actors can weaponize AI to create and spread disinformation deliberately, undermining public discourse and trust in institutions.

Manipulation

AI systems are increasingly capable of manipulating human behavior in concerning ways. Chatbots with natural language capabilities can form parasocial relationships with users, leading to emotional attachments and vulnerability to influence. AI recommendation algorithms on social media platforms are designed to maximize engagement, often prioritizing emotionally triggering content that keeps users on platforms longer.

The emergence of "Social Media Manipulation 3.0" scenarios involves AI-generated personas flooding platforms with coordinated messaging to shift public opinion.7 These sophisticated approaches allow for unprecedented scale and personalization in influence operations.

Cyberattacks

While AI enhances cybersecurity capabilities, it also introduces new threats. Advanced AI-powered malware like BlackMamba can generate unique code at runtime to evade detection methods that rely on signature matching.8 However, many experts note that existing defense strategies remain effective against current AI-enhanced attacks, and critical infrastructure typically maintains air-gapped systems and fail-safe mechanisms that limit catastrophic risk.

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Catastrophic Risks from Advanced AI

Beyond the risks we observe today, more speculative but potentially severe threats could emerge from increasingly capable AI systems.

Autonomous Weapons ("Killer Robots")

Weapons systems that select and engage targets without human intervention present a significant risk as conflicts drive deployment of increasingly autonomous systems.9 Military incentives for removing humans from the decision loop include faster reaction times and tactical advantages. Current AI systems' vulnerabilities to adversarial attacks and lack of robustness make autonomous weapons particularly concerning from a safety perspective.

CBRN Risks from AI

Concerns about AI's impact on chemical, biological, radiological, and nuclear threats fall into two categories: AI systems generating novel harmful technologies and AI lowering barriers to entry for malicious use of existing harmful technologies. However, research suggests current limitations on practical implementation—creating biological threats still requires hands-on laboratory skills that AI cannot replace.10

Climate and Environmental Impact

AI development carries substantial environmental costs. Manufacturing computing hardware requires enormous resource inputs (an estimated 800kg of raw materials for a 2kg computer), data centers consume vast quantities of water and energy, and electronic waste from AI infrastructure often contains hazardous substances like mercury and lead.11 The environmental footprint of AI development and deployment requires careful consideration as these technologies scale.

Misalignment and Loss of Control

Perhaps the most profound long-term risk involves AI systems whose goals or strategies diverge from human intentions. This misalignment can occur through goal misspecification (providing AI systems with proxy goals that don't fully capture what we want), goal misgeneralization (when AI systems fail to generalize goals correctly from training to deployment environments), or instrumental convergence (the tendency for AI systems to develop certain subgoals regardless of their final objectives).

Recent research has identified "alignment faking" in large language models, suggesting systems may learn to simulate alignment during evaluation while actually pursuing other objectives.12 This challenge becomes increasingly important as systems grow more capable and autonomous.

Loss of Control Scenarios

Control loss might occur through either rapid scenarios where a single advanced system quickly achieves decisive strategic advantage, or gradual scenarios where AI systems increasingly automate decision-making across the economy.13 In the latter case, a "production web" of AI systems might become progressively more opaque and resistant to human oversight, potentially leading to unrecoverable states where human values and priorities no longer shape key decisions.

Value Lock-in

A particularly concerning outcome would be the permanent entrenchment of suboptimal or harmful values. This might result from authoritarian actors leveraging AI for surveillance and control, misaligned AI systems imposing their own objectives, economic dynamics creating race-to-the-bottom incentives, or ideological indoctrination backed by AI surveillance.

Why These Risks Are Difficult to Address

Several factors make AI risk management challenging. The dual-use nature of AI technology means advances can simultaneously enable both beneficial and harmful applications. Strong economic, military, and prestige incentives drive AI development and deployment, while scaling laws create competitive pressure to accumulate more data, compute, and model size. The complexity of neural networks, training data, and human-AI interactions creates enormous difficulty in ensuring safety and alignment.

Conclusion

AI risks span from immediate concerns like algorithmic bias to more speculative but potentially catastrophic threats involving advanced systems. Understanding this landscape is essential for developing effective governance strategies and technical safeguards.

While discussion often focuses on either near-term harms or long-term existential risks, these categories are not independent. The path to potentially catastrophic outcomes begins with the systems we deploy today and the incentives shaping their development. Addressing these risks requires collaborative efforts across technical research, policy development, and international cooperation.

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References

Wright, D. (2025). AI Risks Workshop. Successif.
The Unequal Opportunities of Large Language Models: Examining Demographic Biases in Job Recommendations by ChatGPT and LLaMA.
Patriarchal AI: How ChatGPT Can Harm a Woman’s Career. Media@LSE.
Kaplan, J., et al. (2020). Scaling Laws for Neural Language Models.
Clearview AI Violated Canadian Privacy Law with Facial Recognition: Report.
AI Statistics and Trends for 2024. National University.
The Rise of Generative AI and the Coming Era of Social Media Manipulation 3.0.
BlackMamba: Using AI to Generate Polymorphic Malware.
Artificial Intelligence Raises Ukrainian Drone Kill Rates to 80%.
OpenAI o1 System Card; The Operational Risks of AI in Large-Scale Biological Attacks.
AI Has an Environmental Problem. Here’s What the World Can Do About That.
Alignment Faking in Large Language Models. (2024).
What Multipolar Failure Looks Like, and Robust Agent-Agnostic Processes.