III: Medium: Towards Inclusive Recommendation Systems with Stakeholder Alignment

随着推荐系统继续在日常生活的各个方面影响不同的利益相关者，适应他们不同的目标是至关重要的。传统的推荐方法只专注于优化准确性和相关指标，忽视了其他不同的利益相关者依赖的目标。该项目是将不同利益攸关方的目标纳入推荐系统的设计和部署的系统性努力。这包括确定他们的目标，设计合并和优化不同目标的推荐方法，提高数据质量，以及了解不希望看到的系统行为的驱动因素。这一全面的努力符合推进值得信赖的人工智能的国家利益，并将包括比赛、研讨会和互动演示等外联举措。该项目旨在调查设计一个符合多个利益攸关方目标的推荐系统所需的基本组成部分。为了实现这一目标，研究将(I)开发框架，将数据作为软度量来捕获复杂的、依赖于上下文的目标；(Ii)设计方法以提高数据质量并促进与不同目标的匹配；(Iii)开发博弈论推荐算法，以在不同目标之间实现所有利益相关者都能接受的折衷；以及(Iv)开发将全系统行为归因于向推荐系统提供数据的个人的框架。通过这些努力，该项目显著扩展了机器学习系统对齐机制设计的基础知识，并拓宽了我们对数据在多利益相关者环境中的影响的理解。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.