模拟政策影响：开发一种生成式场景写作方法以评估监管的感知效果
Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulation
AI总结：
本研究开发了一种利用GPT-4生成政策前后场景的方法，通过用户研究评估政策对减轻AI负面影响的感知有效性，并以《欧盟AI法案》第50条为例验证了该方法的实用性。
摘要：
AI技术的快速发展对个人和社会产生了众多未来影响。政策制定者需要迅速做出反应，制定政策以减轻这些影响。然而，预测政策的有效性是一项困难的任务，因为某些影响可能仅在未来的发展中才会显现，且相应的政策可能不适用于AI的未来发展。在本研究中，我们开发了一种方法，利用大型语言模型（LLMs）来评估特定政策在减轻特定负面影响方面的有效性。我们通过使用GPT-4生成政策引入前后的场景，并将这些生动的故事转化为基于人类对影响感知的指标。我们利用已建立的生成式AI在媒体环境中的影响分类法，生成了一组场景对，这些场景对既包括被《欧盟AI法案》第50条透明度政策所减轻的，也包括未被减轻的。随后，我们进行了一项用户研究（n=234），从四个风险评估维度评估这些场景：严重性、合理性、规模以及对弱势群体的特定性。我们发现，这项透明度立法在减轻劳动和福祉等领域的危害方面被认为是有效的，但在社会凝聚力和安全等领域则基本无效。通过这一案例研究，我们展示了我们方法的有效性，作为一种工具，可用于迭代评估政策在减轻各种负面影响方面的有效性。我们期望这种方法对希望头脑风暴不同政策或其他缓解策略潜在效用的研究人员或其他利益相关者有所帮助。
英文摘要：
The rapid advancement of AI technologies yields numerous future impacts on individuals and society. Policymakers are tasked to react quickly and establish policies that mitigate those impacts. However, anticipating the effectiveness of policies is a difficult task, as some impacts might only be observable in the future and respective policies might not be applicable to the future development of AI. In this work we develop a method for using large language models (LLMs) to evaluate the efficacy of a given piece of policy at mitigating specified negative impacts. We do so by using GPT-4 to generate scenarios both pre- and post-introduction of policy and translating these vivid stories into metrics based on human perceptions of impacts. We leverage an already established taxonomy of impacts of generative AI in the media environment to generate a set of scenario pairs both mitigated and non-mitigated by the transparency policy in Article 50 of the EU AI Act. We then run a user study (n=234) to evaluate these scenarios across four risk-assessment dimensions: severity, plausibility, magnitude, and specificity to vulnerable populations. We find that this transparency legislation is perceived to be effective at mitigating harms in areas such as labor and well-being, but largely ineffective in areas such as social cohesion and security. Through this case study we demonstrate the efficacy of our method as a tool to iterate on the effectiveness of policy for mitigating various negative impacts. We expect this method to be useful to researchers or other stakeholders who want to brainstorm the potential utility of different pieces of policy or other mitigation strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
