Adaptive Incentive Engineering in Citizen-Centric AI

Adaptive incentives are a valuable tool shown to improve the efficiency of complex multiagent systems and could produce win-win situations for all stakeholders. However, their application usage is very limited, partly due to a significant gap between the literature and practice. We argue that overcoming this gap requires addressing four open research challenges. First, the dynamic, volatile and uncertain nature of environments needs to be fully considered. Second, social factors including user acceptance, fairness, ethical considerations and trust have to match end users' expectations and needs. Third, the evaluation of mechanisms and systems has to be robust and focused on real-world outcomes and stakeholder requirements. Finally, all this has to be built on a reliable theoretical foundation. In order to overcome these open challenges in adaptive incentive engineering, tools from the fields of mechanism design and game theory can be used. This will help to achieve the opportunities adaptive incentives can provide to real-world practical environments, producing better AI systems for the benefit of all.