待翻譯：The Beginner's Guide to AI Governance

AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。

Introduction I recently finished the London School of Economics' programme on AI Law, Policy, and Governance. What follows is a series built from the notes I took along the way. This has been written for people who want to understand how AI is being regulated without reading the regulations themselves. The seven lessons roughly follow the six modules of the course, but I have split and rearranged the sequence where it made more sense to do so. I also added material to cover recent updates in the world of AI, since the info provided in the LSE course itself was current as of early to mid 2025. This is not a substitute for the programme, so if this subject interests you, I do recommend you follow the course. Nothing here is endorsed by LSE, and the notes reflect my own reading on the subject. AI governance moves very quickly, so some material will go out of date in a few months - if you see something that is dated, please reach out to me here. The guide is divided into seven lessons: Lesson 1: Why AI is a policy problem Lesson 2: How AI rules get made Lesson 3: Six ways to govern AI Lesson 4: Why the EU wrote the AI Act Lesson 5: Complying with the EU AI Act Lesson 6: China, the UK, and the US Lesson 7: The International arena and the future of AI governance Lesson 1: Why AI is a policy problem A lot of people carry misconceptions about AI from things they see on films, or see on headlines. In the programme we learnt that AI is not simply another software that can answer your questions, and neither a "sentient presence", a robot that can make decisions on our behalf. AI is the use of computational algorithms to interrogate data at speeds no human can match, learning through mathematics, and analysing and recommending in ways people cannot. So, searching for a word on this document is not AI, while Spotify serving you a playlist assembled from your listening history, and what other millions of users have preferred, is AI. The point here is that "unless you've agreed to define AI in a common way", setting rules or frameworks for governance becomes very difficult, in Professor Evans' own words. A GPT, moving unusually fast Economists call innovations that shape entire economies rather than single industries as 'general purpose technologies', GPTs. Steam power, the internal combustion engine, electricity, IT, the internet, these are all GPTs. Jovanovic and Rousseau back in 2005, identified three markers of GPTs: Pervasiveness - it shows up across industries and sectors Improvement - performance rises over time while the cost of use stays low Innovation spawning - it makes new products and processes possible that weren't feasible before Artificial Intelligence has all three, but what makes it different from its predecessors is the speed of adoption. While electricity needs grids, and computing needed hardware, networks, and services, AI runs on general purpose technologies that already exist. There is no infrastructure lag to slow diffusion down. N.B - The 'GPT' in general purpose technology is an economics term that predates AI entirely. It has nothing to do with the generative pre-trained transformer architecture behind OpenAI's ChatGPT. The macro picture Work - General purpose AI models can automate a lot of parts of jobs that depend on thinking, learning, memory etc. Roles with high exposure to this, like telemarketing or clerical work, are the most obvious. Reports of AI ending human labour are likely to be greatly exaggerated, and new roles will emerge that don't currently exist. At the same time, displaced workers actually reaching these new roles depends on reskilling rates and individual worker characteristics, which is a very different claim from "it will all work out". Employers face their own version of the problem too - the productivity gains with AI are real, but only if the workforce is trained and workflows are redesigned. There are also ethical costs to manage like job losses, AI-enabled surveillance that can erode workers rights, and management by algorithm, which strips employees of their agency in how they do their own work. Regulatory competition - A global study on public trust found that 71% of people expect AI to be regulated, which aligns with an accompanying finding (Gillespie et al., 2023), that 61% believe that AI's long-term impact on society is uncertain and unpredictable. Governments hear this, but because each country or jurisdiction sets its own rules, the field has become ripe for regulatory competition. States are building more favourable environments to attract investment, and companies engage in regulatory arbitrage by simply relocating to the friendliest one. There is a positive thing in being the first in this space, however. The EU's experience with GDPR produced the "Brussels Effect", where large tech firms complied with the higher European standard and then applied it globally, because it would be more costly for them to have two different systems rather than one. The EU is now betting that the AI Act does the same thing. Security - AI could really improve threat detection by parsing unstructured data at speed, surfacing insights early. The main three categories of risk are: Miscalculation - LLMs generate fluent language but don't reason at human level, and cannot grasp how cause and effect relate. Over-reliance without a human-in-the-loop practice creates a real risk of misjudgement. Escalation - An AI tool may read an adversary's activity as more hostile than a human would. Add autonomous weapon systems, which lower the effort and cost of conflict, and there's a plausible future where escalation could outpace our ability to de-escalate. Proliferation - Chemical language models can generate novel molecules for drug discovery, and in the wrong hands, for biowarfare. The concern isn't only capability but access, where domain knowledge that once required years of study and experimentation is now far more readily available. The public sector and the tech cold war - Governments want the efficiency gains from AI but carry a heavier duty of care, so the risks around data privacy, algorithmic transparency and discriminatory outcomes carry more consequences. Some have banned specific applications outright, such as facial recognition. Others have taken a lighter approach, with usage policies and codes of conduct built to leave room for innovation while still holding government to its duties on safety, fairness, and transparency. The UK's AI Opportunities Action Plan positions the country as an aspiring "AI superpower" on the back of being the third-largest AI market globally. The Dutch strategy, by contrast, is built around fundamental liberties and ethical boundaries. Underneath all of this sits the US-China rivalry. Both have declared their intention to lead the space, with China aiming to be a global innovation centre in AI by 2030, and both use sanctions, embargoes, subsidies, and state investment as instruments. Other nations are pushed towards picking sides and building self-sufficient tech sectors, which is how techno-nationalism became the defining trait of the era. Despite this, the US and China share an entangled economic relationship and therefore full decoupling remains unlikely. The micro picture If we zoom in, the picture is messier. Hospitality and construction depend on physical work that a human must do or closely supervise. Any gains through AI sit at the margins, such as scheduling shifts, or optimising stock of raw materials, rather than in the work itself. Where the core work is not physical though, AI reaches much further in, with four areas in particular: Regulatory compliance - Machine learning, natural language processing, and predictive analysis can monitor, detect, and prevent regulatory breaches, the financial sector is a prime example here, while generative AI helps businesses navigate the compliance process itself. Capital investment - The two things AI changes in markets are speed and novel insight. Investment strategy has historically relied on structured data; AI can work across unstructured sources, bank announcements, legal documents, social media, financial reports etc, to improve predictive modelling. The same speed is also the risk, particularly during market stress, alongside cyber and market manipulation concerns. Notably, algorithmic trading systems are still used mainly as initial signals for human traders rather than replacements for them. Profitability - Personalised service and round-the-clock chatbots reduce cost and raise service levels. At the same time, integrating AI into existing systems carries an upfront cost, staff need training in genuinely new skills, and workflows have to be redesigned before the technology pays for itself. Human capital - This is where the empirical research is most interesting because it refuses to line up neatly and all point for more ongoing studies about this ever-evolving technology: Strategic consultants using ChatGPT saw significant gains in speed, performance, and in completing the tasks, but only within the "frontier" of the model's capabilities. When the same people used it on everyday tasks sitting outside those capabilities, their performance got worse. Customer support agents using AI resolved 14% more cases. The effect was concentrated among less-experienced, lower-skilled workers, who gained 34%, while experienced staff saw minimal improvement. Freelancers doing writing and design work saw a decrease in both employment and monthly earnings. A track record of high-quality work did not protect them. Nobody agrees what happens next The problem with writing policy aimed at AI is that credible forecasts differ in both the degree and direction where this emerging technology will take us. On economics, Goldman Sachs projected a 7% rise in annual GDP, around $7 trillion over a 10 year period. McKinsey puts the global boost at an annual $17.1 to $25.6 trillion. Daron Acemoglu, a Nobel laureate in economic sciences, estimates 1% GDP growth over ten years. Luciano Floridi argues the whole thing may be another tech bubble on the pattern of dot-com and telecoms. On work, the most dramatic projections have roughly two-thirds of the jobs that were studied exposed to some degree of AI-enabled automation, with generative AI capable of substituting up to a quarter of current work. David Autor suggests AI could instead complement existing skills and help rebuild the middle class. Other research found only around 5% of US firms reporting any change in employment levels at all. On elections, Emilio Ferrara warns that generative AI makes online interference materially more sophisticated. A separate study by Sam Stockwell at the Centre for Emerging Technology and Security found no evidence that AI-enabled disinformation or deepfakes meaningfully affected UK or European election results. Maybe less so now. On capability, Dario Amodei has suggested (in October 2024) that an AI smarter than a Nobel Prize winner could arrive as early as 2026, though he accepts it may take much longer. Sam Altman puts superintelligence at "a few thousand days", with the same caveat. Chomsky, Roberts, and Watumull argue that statistical prediction will always be superficial and fails to emulate genuine intelligence. Gary Marcus holds that AGI isn't imminent and that the real harms are more mundane, like malfunctions, abuse of systems, concentration of power, and enormous resources misallocated to the wrong thing. Each of these positions implies a completely different regulatory agenda. If Amodei is right, we should be planning for mass displacement and maybe some sort of Universal Basic Income. If Marcus is right, we should be writing antitrust policies. We cannot write good rules for a technology whose trajectory the experts cannot agree on, but waiting for an agreement is itself a decision. The EU AI Act is a cautiona [truncated for AI cost control]