We can't mitigate AI risks we've never imagined (with Darren McKee)
1. Embrace Uncertainty in AI Decisions
Acknowledge and lean into the inherent uncertainty when making decisions about unprecedented challenges like advanced AI, recognizing that precise knowledge of future events is often unavailable.
2. Recognize Inaction as a Choice
Understand that choosing to do nothing in the face of uncertainty is not neutral; it implicitly supports the stance of those who believe no action is needed.
3. Act Early on Long-Term Problems
Recognize that the world takes years, often decades, to organize and address major problems, so begin addressing potential issues like AI risk now, even if they seem years away.
4. Prioritize Over-Preparation for Risks
For high-impact, uncertain risks, consider whether it’s better to be overprepared rather than underprepared, especially given the long timeframes required for societal organization.
5. Maintain Hope Amidst Uncertainty
Recognize that uncertainty cuts both ways; unsolved problems are not necessarily unsolvable, fostering hope that solutions can be found if actively pursued.
6. Persist Despite Low Odds
Even if the probability of success is low (e.g., 10%), it is still worth trying to address critical issues, as the alternative of not trying at all is worse.
7. Actively Seek Solutions
Increase the likelihood of finding solutions to complex problems by actively looking for them, rather than succumbing to hopelessness and disengagement.
8. Cultivate Broad Imagination
Actively use imagination to think about what might happen in a wide range of situations, as it’s fundamental to considering what could be different from what is, for both novel and recurrent events.
9. Deeply Analyze Plausibility
When considering possibilities, counteract availability bias by consciously thinking through scenarios in more detail, rather than letting immediate feelings of plausibility anchor your beliefs.
10. Anticipate Rapid Global Shifts
Be prepared for important global events to happen within a very short period of time and catch almost everyone off guard, as demonstrated by past events like the COVID-19 pandemic.
11. Avoid Analysis Paralysis
While embracing cognitive humility from foresight is good, avoid falling into analysis paralysis where uncertainty leads to feeling unable to act, as inaction still supports a particular outcome.
12. Practice Strategic Foresight
Employ foresight as a mental tool to explore different plausible futures, focusing on what could be rather than just what is probable, to challenge assumptions and aid imagination.
13. Analyze Multi-Order Consequences
When considering new technologies or events, analyze first, second, and third-order effects by repeatedly asking “what if that happens?” to explore the full possibility space and implications.
14. Perform Pre-Mortem Analysis
Before a project or a future event, conduct a “pre-mortem” exercise by imagining it has already failed or succeeded, then work backward to identify the reasons why, to better prepare.
15. Challenge Personal Assumptions
Engage in exercises like foresight and pre-mortems to regularly challenge your own assumptions about the world and aid your imagination.
16. Discuss AI Timelines Nuancedly
Engage in nuanced conversations about AI timelines, considering projections of computational power and recent advances in capabilities, rather than binary “risky” or “not risky” stances.
17. Focus on AI’s Unique Dangers
Understand that AI’s unique dangers stem from its unprecedented speed of operation, its ability to gain conceptual insight, and its rapidly increasing capabilities.
18. Distinguish AI Alignment & Control
Clearly differentiate between AI alignment (AI doing what we want in the way we want) and AI control (our ability to stop it if it doesn’t), as both are critical for safety.
19. Address Interconnected AI Risks
Recognize that AI safety concerns (alignment, control, speed of development) are interconnected; solving any major plank can significantly address the others.
20. Grasp AI Integration’s Control Impact
Recognize that as AI becomes increasingly powerful and integrated into daily life (like phones or social media), human control over it will likely diminish, even if initially willingly adopted.
21. Anticipate High AI Disengagement Costs
Understand that once AI becomes deeply integrated into society, the world will restructure around it, making it difficult and costly to disengage or “shut it down.”
22. Recognize Kill Switch Disincentives
Understand that creating a “kill switch” for deeply integrated technologies like the internet or advanced AI introduces significant new risks (e.g., optimal target for malicious actors), creating disincentives to implement them.
23. Address Distributed AI Control
When considering control over advanced AI, account for its potential to be distributed across many servers and countries, complicating jurisdictional responsibility and the ability to act.
24. Prepare for AI’s Novel Discoveries
Be prepared for AI systems to discover new insights or ways the world works that humans currently don’t understand, as this capability makes it hard to protect against unforeseen consequences.
25. Focus on AI Impact, Not Consciousness
When assessing AI risks, focus on its potential to cause harm, recognizing that consciousness is not a prerequisite for an AI to be dangerous (similar to a virus).
26. Focus on AI’s Functional Behavior
When discussing AI, avoid getting sidetracked by philosophical debates on whether AIs “truly” have goals or intelligence; instead, focus on how they act as if they have goals and demonstrate intelligence.
27. Invest in AI Interpretability
Support and work on mechanistic interpretability and understanding how AI systems make decisions, as this helps address a wide range of problems from present biases to future risks.
28. Advocate for Robust AI Governance
Advocate for comprehensive AI governance measures including auditing and evaluation schemes, licensing requirements, increased transparency and security for AI companies, and compute governance to track powerful AI chips.
29. Implement Pre-Training Safety Measures
Require AI companies to implement and pass safety measures not just before deployment, but also before training, thoroughly analyzing and evaluating models for potential harm.
30. Demand Predictive AI Capability Statements
Require AI companies to detail predicted system capabilities at different training levels; a strong track record of accurate predictions builds trust, while consistent inaccuracies suggest less leeway for development.
31. Scrutinize AI Power Concentration
Be aware of and scrutinize the concentration of power in the hands of a few individuals or companies developing advanced AI, as they wield outsized influence over global outcomes.
32. Foster Democratic AI Value Discussions
To address the challenge of aligning AI with diverse human values, engage in more facilitated conversations within democratic spaces to collectively muddle through complex moral philosophy.
33. Engage in Multi-Faceted Advocacy
Contribute to AI safety by engaging in political advocacy, talking to representatives, raising awareness, volunteering, donating, or working directly in the AI safety field.
34. Balance Specificity in Risk Communication
When communicating about risks, find a delicate balance between providing concrete details to aid imagination and avoiding overly specific scenarios that are likely to be wrong and easily criticized.
35. Diversify Communication Messages
To reach diverse audiences with varying preferences for detail, put out multiple messages about complex topics like AI risk.
36. Leverage Art for Risk Awareness
Utilize art forms like filmmaking and storytelling to help people understand and feel invested in complex risks like pandemics or AI, but be cautious of unrealistic elements or misinterpretations.
37. Avoid Stereotypical AI Imagery
When discussing AI, avoid stereotypical robotic imagery that can inaccurately represent the abstract and less tangible nature of AI risks.
38. Explain Complexities with Analogies
When explaining complex topics, extract key concepts and present them using general, relatable analogies (e.g., reward hacking as stuffing things in a drawer) to enhance understanding.
39. Avoid Jargon in Communication
When communicating complex topics, avoid jargon and technical shortcuts; instead, think about how to explain concepts to someone with no prior exposure.
40. Be Concise in Communication
Practice conciseness in communication by simply omitting what you don’t intend to say, rather than explicitly stating what you won’t cover.
41. Use Relatable Examples
Test examples on diverse people and use analogies grounded in familiar life experiences (e.g., music, food, personal growth) to make complex ideas more accessible.
42. Personal Growth as Intelligence Analogy
Reflect on your own personal growth from childhood to adulthood as an analogy for the vast, unimaginable leaps in capability that superintelligence might represent.
43. Structure Content for Retention
To enhance accessibility and memory retention, structure content with clear overviews at the beginning and key message takeaways at the end of each section or chapter.
44. Engage in Nuanced One-on-One Dialogue
To understand complex issues and reduce tribalism, engage in longer, one-on-one conversations with people, allowing for more nuanced and sophisticated expression of concerns than short social media posts.
45. Identify Overlapping Policy Solutions
To overcome tribalism in complex issues like AI safety, encourage different “teams” to list their policy proposals and then identify areas of overlap where collaboration can occur.
46. Stay Informed and Engaged
Acknowledge that concern about AI is warranted, and actively seek out a wide range of voices and information to learn more about this critical topic and find ways to engage.