Translating AI to Impact: Uncertainty and Human-Agent Interactions in Multi-Agent Systems for Public Health and Conservation

Abstract

Artificial intelligence (AI) is now being applied widely in society, including to support decision-making in important, resource-constrained efforts in conservation and public health. Such real-world use cases introduce new challenges, like noisy, limited data and human-in-the-loop decision-making. I show that ignoring these challenges can lead to suboptimal results in AI for social impact systems. For example, previous research has modeled illegal wildlife poaching using a defender-adversary security game with signaling to better allocate scarce conservation resources. However, this work has not considered detection uncertainty arising from noisy, limited data. In contrast, my work addresses uncertainty beginning in the data analysis stage, through to the higher-level reasoning stage of defender-adversary security games with signaling. I introduce novel techniques, such as additional randomized signaling in the security game, to handle uncertainty appropriately, thereby reducing losses to the defender. I show similar reasoning is important in public health, where we would like to predict disease prevalence with few ground truth samples in order to better inform policy, such as optimizing resource allocation. In addition to modeling such real-world efforts holistically, we must also work with all stakeholders in this research, including by making our field more inclusive through efforts like my nonprofit, Try AI.