Collaborative Research: III: Medium: Designing AI Systems with Steerable Long-Term Dynamics

用户通过他们的选择提供的反馈（例如，点击、购买）是容易用于训练自主信息检索和推荐系统的最常见类型的数据之一，并且其被广泛用于在线平台。然而，基于选择数据的天真培训系统可能只能提高短期参与度，而不能提高平台的长期可持续性及其用户，内容提供商和其他利益相关者的长期利益。在这个复杂的问题空间和竞争的利益，这是不可能的，有一个单一的和紧凑的算法解决方案，本质上是公平的或最佳的-出于同样的原因，我们的法律的代码和税收政策填补了相当大的图书馆。相反，该项目开发了一个新的算法框架，以表达AI系统的类似详细政策。该框架为决策者提供了战略干预措施，可预测地引导平台的长期动态，使其不仅在短期内优化参与，而且还反映了监督平台的任何治理系统所设定的长期价值。为了实现这一目标，该项目为人工智能平台引入了一个宏观抽象层，在这个抽象层下，用户满意度、项目公平性、供应商池大小）可以通过宏观干预（例如，曝光分配、新内容的推广政策、反歧视条例）。由于平台在微观层面上发挥作用，该项目开发了新的搜索和推荐方法，最佳地将宏观层面的干预措施分解为一系列微观层面的干预措施（例如，排名）。关键的技术挑战在于弥补宏观一级干预措施（例如，周）和微观层面的干预（例如，个人请求），这是使用机器学习，因果推理和控制理论来解决的。这种表述提供了一个技术抽象层，在宏观层面上降低了人类和自动化决策的复杂性，从而实现了战略推理和行动。最后，由于任何级别的最佳行动都依赖于无偏和准确的估计，该项目开发了新的估计器，以抵消反馈回路中的偏差。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

The feedback that users provide through their choices (e.g., clicks, purchases) is one of the most common types of data readily available for training autonomous information retrieval and recommendation systems, and it is widely used in online platforms. However, naively training systems based on choice data may only improve short-term engagement, but not the long-term sustainability of the platform and the long-term benefits to its users, content providers, and other stakeholders. In this complex space of problems and competing interests, it is unlikely that there is a single and compact algorithmic solution that is inherently fair or optimal --- for the same reason that our legal codes and tax policies fill sizable libraries. Instead, the project develops a new algorithmic framework to express similarly detailed policies also for AI systems. This framework provides decision-makers with strategic interventions that predictably steer the long-term dynamics of a platform so that they not only optimize engagement in the short term but additionally reflect long-term values set by whatever system of governance oversees the platform. To achieve this goal, the project introduces a macroscopic layer of abstraction for AI platforms under which long-term objectives (e.g., user satisfaction, item fairness, supplier pool size) can be measured and influenced through macroscopic interventions (e.g., exposure allocation, promotion policies for new content, anti-discrimination regulation). Since platforms act at the microscopic level, the project develops new search and recommendation methods that optimally break macro-level interventions into a sequence of micro-level interventions (e.g., rankings). The crucial technical challenge lies in bridging the mismatch in time scales between macro-level interventions (e.g., weeks) and micro-level interventions (e.g., individual requests), which is addressed using machine learning, causal inference, and control theory. This formulation provides a technical layer of abstraction that reduces complexity for both human and automated decision-making at the macro level, enabling strategic reasoning and action. Finally, since optimal actions at any level rely on unbiased and accurate estimates, the project develops new estimators that counteract biases in feedback loops.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.