III: Medium: Towards Inclusive Recommendation Systems with Stakeholder Alignment

As recommender systems continue to impact diverse stakeholders in various aspects of daily life, accommodating their distinct objectives is crucial. Traditional recommendation methodologies have focused solely on optimizing accuracy and related metrics, neglecting other diverse stakeholder-dependent objectives. This project represents a systematic effort to incorporate the objectives of different stakeholders into the design and deployment of a recommender system. This includes characterizing their objectives, designing recommendation approaches that incorporate and optimize different objectives, improving data quality, and understanding the drivers of undesired system behaviors. This comprehensive effort serves the national interest of advancing trustworthy AI and will include outreach initiatives such as competitions, workshops, and interactive demonstrations. This project aims to investigate the fundamental components necessary for designing a recommender system that aligns with the objectives of multiple stakeholders. To achieve this, the research will (i) develop frameworks that use data as a soft metric to capture complex, context-dependent objectives; (ii) design methods to improve data quality and facilitate the alignment with different objectives; (iii) develop game-theoretic recommendation algorithms to achieve a tradeoff in different objectives that is acceptable to all stakeholders; and (iv) develop frameworks that attribute system-wide behaviors to individuals who provide data to a recommender system. Through these efforts, this project significantly expands the foundational knowledge of alignment mechanism design for machine learning systems and broadens our understanding of the impact of data in a multi-stakeholder environment. 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.