Which insight, approach, or paradigm will be the primary key to successfully aligning Artificial Superintelligence (ASI)?

rationale:

The resolution criteria establish a demanding threshold: an 'overwhelming consensus' that a single approach is the 'primary mechanism' for aligning Artificial Superintelligence (ASI). However, the strongest signal from 2025–2026 primary sources is that leading labs frame alignment as a defense-in-depth or 'Swiss cheese' problem requiring a portfolio of layered safeguards. Anthropic’s 'Recommended Directions', the 2026 Singapore Consensus aisafetypriorities.org, and the International AI Safety Report 2026 internationalaisafetyreport.org all emphasize pluralistic approaches over a single silver bullet. Because future consensus will likely credit an integrated synthesis or a conceptual breakthrough not adequately described by today’s narrow categories, 'A fundamentally new insight or paradigm not yet formulated' holds the plurality of the probability mass at 26%.

Among existing, named paradigms, Scalable Oversight / Debate / Amplification (19%) is the strongest contender. This approach directly targets the core technical bottleneck of ASI: humans cannot reliably supervise superhuman cognition. OpenAI’s Superalignment agenda openai.com, Anthropic’s focus on automated alignment researchers and weak-to-strong generalization 2 sources, and DeepMind’s emphasis on amplified oversight arxiv.org all structurally position this paradigm as the mainline path to supervising systems smarter than their creators.

Mechanistic Interpretability (18%) follows closely due to significant institutional momentum as a robust verification layer. Prominent advocacy (such as Dario Amodei's framing of interpretability as an 'MRI' for catching deception darioamodei.com) positions it as an indispensable diagnostic. However, current circuit tracing and attribution methods only capture a fraction of computation and require extensive human effort anthropic.com. It is currently better modeled as a crucial control enabler rather than the standalone primary mechanism for goal alignment.

RLHF / Constitutional AI / Preference Learning (8%) is today's deployed workhorse (e.g., RLAIF anthropic.com), but primary sources explicitly warn against its scalability. The International AI Safety Report 2026 notes that human feedback is constrained by human error and bias, leading to reward hacking and sycophancy internationalaisafetyreport.org. It will almost certainly be superseded or subsumed by scalable oversight in the superhuman regime.

Corrigibility (8%) and Eliciting Latent Knowledge (ELK) / Honest AI (8%) represent vital conceptual niches. ELK remains central to mapping between an AI's world model and human understanding alignment.org, and the UK AISI heavily prioritizes honesty as systems scale aisi.gov.uk. Corrigibility could emerge as the primary key if the field pivots from 'learning human values' to 'reliable deferral by construction.'

Finally, Formal Verification (5%), Value Learning (5%), and Cooperative / multi-agent approaches (3%) are assigned lower probabilities. Formal proofs currently fail to scale to large deep neural networks and rely heavily on deployment assumptions internationalaisafetyreport.org. Value learning has largely been absorbed into preference learning, and cooperative AI, while critical for mitigating ecosystem conflict cooperativeai.com, serves as a complement rather than a primary mechanism for single-system alignment.