A follow-up thought based on conversations catalyzed by this post:
Much of the research on governing AI and managing its potential unintended consequences currently falls into two ends of a spectrum related to assumptions of the imminence of transformative AGI. Research operating under the assumption of a high probability of near-term transformative AI (e.g., within 10-15 years) is typically focused more on how to align AGI with ideal aggregations of human preferences (through yet to be tested aggregation processes). Research operating under the assumption of a low probability of near-term transformative AI is typically focused on how to reduce discriminatory, safety, and privacy harms posed by present-day (relatively “dumb”) AI systems. The proposal in this post seeks a framework that, over time, bridges these two important ends of the AI safety spectrum.
This was cross-posted here as well:
https://law.stanford.edu/2022/09/25/aligning-ai-with-humans-by-leveraging-law-as-data/
https://www.lesswrong.com/posts/9xR4KExLQKNK4iggc/leveraging-legal-informatics-to-align-ai
A follow-up thought based on conversations catalyzed by this post:
Much of the research on governing AI and managing its potential unintended consequences currently falls into two ends of a spectrum related to assumptions of the imminence of transformative AGI. Research operating under the assumption of a high probability of near-term transformative AI (e.g., within 10-15 years) is typically focused more on how to align AGI with ideal aggregations of human preferences (through yet to be tested aggregation processes). Research operating under the assumption of a low probability of near-term transformative AI is typically focused on how to reduce discriminatory, safety, and privacy harms posed by present-day (relatively “dumb”) AI systems. The proposal in this post seeks a framework that, over time, bridges these two important ends of the AI safety spectrum.