Building Blocks for AI Governance
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Bibliographic details
- Authors: Ian Bremmer, MUSTAFA SULEYMAN
- Published: December 1, 2023
Executive summary
- Artificial intelligence will open people’s lives and societies to groundbreaking scientific advances, unprecedented access to technology, toxic misinformation that disrupts democracies, and economic upheaval.
- AI will trigger a fundamental shift in the structure and balance of global power, creating an unparalleled challenge for political institutions around the world.
- Policymakers must adhere to five guiding principles to govern AI effectively.
The AI power paradox and technological dynamics
- AI is hyper-evolutionary: its rate of improvement will far surpass Moore’s Law (doubling of computing power every two years).
- The amount of computation used to train the most powerful AI models has increased by a factor of 10 every year for the past 10 years.
- Processing that once took weeks now happens in seconds; foundation technologies will become smaller, cheaper, and more accessible.
- AI systems may soon be capable of quasi-autonomy and may become the first technology with the means to improve on itself.
- Proliferation characteristics:
- AI algorithms are far easier and cheaper to copy and share (or steal) than physical assets.
- Models will soon run on smartphones as computing gets cheaper.
- Marginal cost of models and marginal cost of delivery is zero, meaning once released, AI models can and will be everywhere.
- Most models will be safe; but a single malign or “breakout” model could wreak havoc.
Geopolitical and commercial incentives that undermine governance
- Dual-use nature: AI’s civil and military uses are blurred, but unlike nuclear enrichment, AI’s low cost enables endless deployment for civil or military use.
- AI supremacy is a strategic objective of every government and company with resources to compete; the US and China both see AI supremacy as a strategic objective that must be achieved—and denied to the other.
- This zero-sum dynamic incentivizes acceleration of AI development rather than slowing it down.
- Control and enforcement challenges:
- Every aspect of AI is currently controlled by the private sector.
- A handful of large tech firms control AI today, but gradual proliferation may bring many small players into the space, complicating governance.
- Private businesses and individual technologists have limited incentive to self-regulate.
- Conclusion: The combination of technological features and incentives renders traditional governance models inadequate.
Five guiding principles for AI governance
- Precautionary
- The risk-reward profile of AI is asymmetric; policymakers must guard against potentially catastrophic downsides.
- The precautionary principle needs to be adapted to AI and enshrined in any governance regime.
- Agile
- AI governance must be as agile, adaptive, and self-correcting as AI is fast-moving, hyper-evolutionary, and self-improving.
- Static policymaking structures that prize stability and predictability will not work.
- Inclusive
- Regulators must collaborate with the commercial sector; the only way to properly oversee AI is to collaborate with private technology companies.
- Governments should make companies parties to international agreements to reflect the borderless nature of AI.
- Including private companies in high diplomacy may be unprecedented but excluding them would doom governance structures.
- Impermeable
- Given AI’s ease of proliferation, compliance mechanisms should be watertight, with easy entry to compel participation and costly exit to deter noncompliance.
- One defection from a regime could allow a dangerous model to escape.
- Targeted
- AI governance must be targeted, risk-based, and modular rather than one-size-fits-all.
- Develop a live, working taxonomy of discrete potential AI impacts to determine which tools target which risks.
Institutional implications and recommended approach
- AI governance must account for unpredictability: rules passed today may not be effective or relevant in months or years.
- Avoid boxing regulators with inflexible regimes; instead build institutions and conceptual frameworks that enable adaptive policy.
- Policymaking should avoid a false binary between progress and doom; the goal is to stimulate innovation while avoiding dangerous proliferation and geopolitical arms-race dynamics.
- The international community needs to agree on a conceptual framework for how to think about AI; the five principles are offered as a start.
Ian Bremmer and MUSTAFA SULEYMAN, F&D Magazine, December 2023
Content in this bundle
- Point of View: Building Blocks for AI Governance