Amid growing concerns over AI's societal risks--ranging from civilizational
collapse to misinformation and systemic bias--this study explores the
perceptions of AI experts and the general US registered voters on the
likelihood and impact of 18 specific AI risks, alongside their policy
preferences for managing these risks. While both groups favor international
oversight over national or corporate governance, our survey reveals a
discrepancy: voters perceive AI risks as both more likely and more impactful
than experts, and also advocate for slower AI development. Specifically, our
findings indicate that policy interventions may best assuage collective
concerns if they attempt to more carefully balance mitigation efforts across
all classes of societal-scale risks, effectively nullifying the
near-vs-long-term debate over AI risks. More broadly, our results will serve
not only to enable more substantive policy discussions for preventing and
mitigating AI risks, but also to underscore the challenge of consensus building
for effective policy implementation.

AI 专家和美国普通选民对 18 种具体 AI 风险的可能性和影响以及管理这些风险的政策偏好进行了调查研究，结果显示选民认为 AI 风险更可能发生且更具有影响力，同时主张减缓 AI 发展，因此政策干预应该平衡各类社会规模风险的减轻努力，从而消除关于 AI 风险近期与长期的争论，并强调达成有效政策实施的共识建立的挑战。

公众对社会规模 AI 风险感知的治理意涵

Implications for Governance in Public Perceptions of Societal-scale AI  Risks

We explore the AI2050 "hard problems" that block the promise of AI and cause
AI risks: (1) developing general capabilities of the systems; (2) assuring the
performance of AI systems and their training processes; (3) aligning system
goals with human goals; (4) enabling great applications of AI in real life; (5)
addressing economic disruptions; (6) ensuring the participation of all; (7) at
the same time ensuring socially responsible deployment; (8) addressing any
geopolitical disruptions that AI causes; (9) promoting sound governance of the
technology; and (10) managing the philosophical disruptions for humans living
in the age of AI. For each problem, we outline the area, identify significant
recent work, and suggest ways forward. [Note: this paper reviews literature
through January 2023.]

AI2050 年的 “硬问题” 会阻止 AI 的发展，并引发 AI 的风险。其中包括系统的全面能力开发、确保 AI 系统和训练过程的性能、使系统目标与人类目标相一致、在实际生活中实现出色的 AI 应用以及应对经济颠覆。

人工智能中的十个难题

Ten Hard Problems in Artificial Intelligence We Must Get Right

Artificial Intelligence (AI) has made impressive progress in recent years and
represents a key technology that has a crucial impact on the economy and
society. However, it is clear that AI and business models based on it can only
reach their full potential if AI applications are developed according to high
quality standards and are effectively protected against new AI risks. For
instance, AI bears the risk of unfair treatment of individuals when processing
personal data e.g., to support credit lending or staff recruitment decisions.
The emergence of these new risks is closely linked to the fact that the
behavior of AI applications, particularly those based on Machine Learning (ML),
is essentially learned from large volumes of data and is not predetermined by
fixed programmed rules.
Thus, the issue of the trustworthiness of AI applications is crucial and is
the subject of numerous major publications by stakeholders in politics,
business and society. In addition, there is mutual agreement that the
requirements for trustworthy AI, which are often described in an abstract way,
must now be made clear and tangible. One challenge to overcome here relates to
the fact that the specific quality criteria for an AI application depend
heavily on the application context and possible measures to fulfill them in
turn depend heavily on the AI technology used. Lastly, practical assessment
procedures are needed to evaluate whether specific AI applications have been
developed according to adequate quality standards. This AI assessment catalog
addresses exactly this point and is intended for two target groups: Firstly, it
provides developers with a guideline for systematically making their AI
applications trustworthy. Secondly, it guides assessors and auditors on how to
examine AI applications for trustworthiness in a structured way.

人工智能（AI）在最近几年取得了显著进展，对经济和社会产生了重大影响。然而，仅当 AI 应用根据高质量标准开发并有效地防范新的 AI 风险时，AI 和基于其的商业模式才能充分发挥其潜力。此论文致力于解决 AI 应用可信度的问题，通过提供一个可信 AI 评估目录，旨在帮助开发人员和审计员以结构化的方式评估 AI 应用程序的可信度。