We analyze how output and wages behave under different scenarios for
technological progress that may culminate in Artificial General Intelligence
(AGI), defined as the ability of AI systems to perform all tasks that humans
can perform. We assume that human work can be decomposed into atomistic tasks
that differ in their complexity. Advances in technology make ever more complex
tasks amenable to automation. The effects on wages depend on a race between
automation and capital accumulation. If the distribution of task complexity
exhibits a sufficiently thick infinite tail, then there is always enough work
for humans, and wages may rise forever. By contrast, if the complexity of tasks
that humans can perform is bounded and full automation is reached, then wages
collapse. But declines may occur even before if large-scale automation outpaces
capital accumulation and makes labor too abundant. Automating productivity
growth may lead to broad-based gains in the returns to all factors. By
contrast, bottlenecks to growth from irreproducible scarce factors may
exacerbate the decline in wages.

我们分析了在可能达到人工智能系统能执行人类所有任务的技术进步背景下，产出和工资的表现，并且假设人类的工作可以分解为不同复杂度的原子任务。技术进步使得越来越复杂的任务能够自动化。工资的影响取决于自动化与资本积累之间的竞赛。如果任务复杂度的分布具有足够厚的无穷尾部，那么总是有足够的工作供人类从事，而工资可能会永久上升。相反，如果人类能够执行的任务复杂度是有界的，并且实现了完全自动化，那么工资会崩溃。但即使在这之前，如果大规模自动化超过资本积累，使得劳动力过剩，工资也可能会下降。自动化生产率的增长可能会导致对所有要素的广泛利益增长。相反，来自不可再生稀缺要素的增长瓶颈可能会加剧工资的下降。

向通用人工智能的过渡场景

Scenarios for the Transition to AGI

Algorithmic fairness has gained prominence due to societal and regulatory
concerns about biases in Machine Learning models. Common group fairness metrics
like Equalized Odds for classification or Demographic Parity for both
classification and regression are widely used and a host of computationally
advantageous post-processing methods have been developed around them. However,
these metrics often limit users from incorporating domain knowledge. Despite
meeting traditional fairness criteria, they can obscure issues related to
intersectional fairness and even replicate unwanted intra-group biases in the
resulting fair solution. To avoid this narrow perspective, we extend the
concept of Demographic Parity to incorporate distributional properties in the
predictions, allowing expert knowledge to be used in the fair solution. We
illustrate the use of this new metric through a practical example of wages, and
develop a parametric method that efficiently addresses practical challenges
like limited training data and constraints on total spending, offering a robust
solution for real-life applications.

通过将与预测相关的分布属性纳入考虑，我们扩展了人口平等的概念，允许专家知识在公平解决方案中得到应用，并通过一个工资的实际案例说明了此新度量的使用，同时开发了解决实际挑战的参数方法，提供了一个适用于现实生活中应用场景的强大解决方案。