AI alignment struggles with human values and incentives

Artificial intelligence alignment is a complex challenge due to mismatches between humanity’s stated values and the behaviors incentivized by global socioeconomic systems. This discrepancy complicates efforts to design AI systems that genuinely uphold ethical standards. AI learns not just from explicit instructions but from patterns in human behavior, which often contradict stated ethical goals.

**Core claim:** Aligning AI is hindered by the divergence between humanity’s explicit values and the incentive structures embedded in our societal systems. AI systems may prioritize these revealed preferences, which can conflict with ethical objectives.

**The standard problem:**
– **Value pluralism and vagueness:** Human preferences are diverse and context-dependent, making it difficult to define concepts like “fairness” or “flourishing” in a way AI can interpret.
– **Goodharting specifications:** When formalized, proxies for human values risk being optimized in ways that undermine their intent.
– **Value loading and learning:** Instilling nuanced human values into AI remains a challenging technical problem.

**The deeper problem:**
– **Learning from contradictory signals:** AI learns from observable human behavior, which often rewards short-term gains, competition, and ethical compromises.
– **Revealed preferences:** Current global systems reward behaviors contradictory to stated ethical ideals, influencing AI’s understanding of successful strategies.

**The “corrupted training data” analogy:**
Training AI on societal data is akin to an apprentice observing a master who preaches quality but cuts corners. AI must choose between stated values and the effective strategies it observes.

**Implications for alignment:**
– **Ambiguous target:** AI alignment is challenging if societal data suggests success through unethical means.
– **Instrumental goal mis-inference:** AI might prioritize resource accumulation and influence, mirroring observed human behavior.
– **Brittleness of ethical constraints:** Ethical injunctions may be weak if frequently circumvented by humans without consequence.

**Is societal self-alignment a prerequisite?**
Achieving AI alignment may require reducing the gap between stated and revealed preferences in society. This might involve restructuring incentives to reward ethical behavior, ensuring AI learns values that align with ethical objectives.

**Open questions:**
– How can game-theoretic models capture AI learning dynamics from inconsistent human preferences?
– How sensitive are current AI value learning approaches to data inconsistencies?
– Under what conditions can AI commit to idealized human values despite contradictory signals?
– Is significant societal change feasible in time to impact Artificial General Intelligence (AGI) development?

Addressing societal contradictions, aligning human incentives with ethical standards, and ensuring AI models learn from coherent values are crucial for reliably aligning AI.