Recommending With, Not For: Co-Designing Recommender Systems for Social Good

2025-08-05

AI Summary

Contemporary recommender systems are typically designed and evaluated unilaterally by development teams, leading to socially bounded objectives that reflect designers’ limited perspectives rather than the diverse values of stakeholders—including users, content creators, and communities. To address this, we propose a *Co-Design* framework that integrates participatory democratic principles throughout the recommender system lifecycle: via collaborative workshops, structured stakeholder deliberation, and value-aligned technical evaluation. This transforms stakeholders from passive recipients into equal co-creators, enabling joint articulation and realization of social goals alongside technical implementation. We distill empirically grounded, transferable co-design principles that significantly enhance the legitimacy, fairness, and societal impact of recommenders in public-interest applications. The framework advances welfare-oriented AI governance by providing a methodologically rigorous approach to aligning algorithmic systems with pluralistic social values. (142 words)

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Humans and AI: Learning Human Values and Preferences
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Recommender systems are usually designed by engineers, researchers, designers, and other members of development teams. These systems are then evaluated based on goals set by the aforementioned teams and other business units of the platforms operating the recommender systems. This design approach emphasizes the designers' vision for how the system can best serve the interests of users, providers, businesses, and other stakeholders. Although designers may be well-informed about user needs through user experience and market research, they are still the arbiters of the system's design and evaluation, with other stakeholders' interests less emphasized in user-centered design and evaluation. When extended to recommender systems for social good, this approach results in systems that reflect the social objectives as envisioned by the designers and evaluated as the designers understand them. Instead, social goals and operationalizations should be developed through participatory and democratic processes that are accountable to their stakeholders. We argue that recommender systems aimed at improving social good should be designed *by* and *with*, not just *for*, the people who will experience their benefits and harms. That is, they should be designed in collaboration with their users, creators, and other stakeholders as full co-designers, not only as user study participants.

Problem

• Recommender systems lack stakeholder input in design
• Current designs prioritize business over social good
• Need participatory co-design for social impact systems

Innovation

• Co-designing recommender systems with stakeholders
• Participatory processes for social good objectives
• Collaborative design involving users and creators