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

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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.