Building Human Values into Recommender Systems: An Interdisciplinary Synthesis (2207.10192v1)

Abstract: Recommender systems are the algorithms which select, filter, and personalize content across many of the worlds largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and studied. Our overarching question is how to ensure that recommender systems enact the values of the individuals and societies that they serve. Addressing this question in a principled fashion requires technical knowledge of recommender design and operation, and also critically depends on insights from diverse fields including social science, ethics, economics, psychology, policy and law. This paper is a multidisciplinary effort to synthesize theory and practice from different perspectives, with the goal of providing a shared language, articulating current design approaches, and identifying open problems. It is not a comprehensive survey of this large space, but a set of highlights identified by our diverse author cohort. We collect a set of values that seem most relevant to recommender systems operating across different domains, then examine them from the perspectives of current industry practice, measurement, product design, and policy approaches. Important open problems include multi-stakeholder processes for defining values and resolving trade-offs, better values-driven measurements, recommender controls that people use, non-behavioral algorithmic feedback, optimization for long-term outcomes, causal inference of recommender effects, academic-industry research collaborations, and interdisciplinary policy-making.

• The paper presents an interdisciplinary framework for embedding human values like fairness and diversity into the design and evaluation of recommender systems.
• It operationalizes abstract values into measurable metrics using techniques such as reinforcement learning and explainable AI for long-term user well-being.
• A detailed case study on political diversity highlights practical challenges and policy implications for aligning technology with societal interests.

Building Human Values into Recommender Systems: An Interdisciplinary Synthesis

The paper "Building Human Values into Recommender Systems: An Interdisciplinary Synthesis" provides a comprehensive examination into how recommender systems can be designed and operated to align with human values. Recommender systems play a pivotal role in selecting, filtering, and personalizing content across major platforms and applications. Given their profound impact, both beneficial and detrimental, on individuals and societies, the paper addresses the critical question of how to ensure that these systems reflect the values of the users and communities they serve.

Key Areas of Exploration

The paper organizes its discussion into several focal areas:

1. Value Identification and Categorization: The authors identify a set of values pertinent to recommender systems, derived from various ethical, policy, and industry sources. These values include usefulness, freedom of expression, privacy, safety, self-expression, well-being, accuracy, fairness, and diversity, among others. Each value is explored in terms of its relevance to recommender systems and potential indicators for measurement.
2. Operationalization of Values: One of the main challenges highlighted is the translation of abstract values into measurable metrics that can guide the design and evaluation of recommender systems. The paper discusses the process of defining metrics that are valid, reliable, fair, and legitimate, stressing the importance of multi-stakeholder involvement in this process.
3. Current Industrial Practice: The paper provides an overview of modern recommender system architectures and operational practices. It describes a typical recommender workflow from content moderation to candidate generation, ranking, and re-ranking, emphasizing the complexity and the multi-faceted nature of these systems.
4. Case Study: Increasing Political Diversity: To illustrate practical implementation, the paper presents a case paper on incorporating the value of political diversity into a news recommender system. This involves steps from researching potential unintended consequences, developing appropriate metrics, considering different product designs, implementing changes, evaluating outcomes, establishing guardrails, and ongoing monitoring.
5. Techniques for Value Integration: Detailed discussions around methods to modify item selection, incorporate user feedback and control, and address multi-stakeholder fairness are provided. Techniques such as reinforcement learning for long-term optimization, explainable AI, and trade-off optimization are considered for better alignment of recommender systems with human values.
6. Policy Implications and Approaches: The paper critiques current policy frameworks, like the EU Digital Services Act, proposing that risk-based and value-sensitive design approaches need to be better integrated. It calls for policies that ensure transparency, accountability, and external audits while recognizing the complexity of implementing such regulatory measures effectively.

Implications and Future Directions

The research detailed in this paper carries significant implications for both theoretical advances and practical implementations in designing recommender systems aligned with human values. Key observations and speculations for future developments include:

• Interdisciplinary Design: Effective value-sensitive design of recommender systems requires a blend of technical sophistication and deep insights from social sciences, ethics, policy, and law. This multidisciplinary approach is crucial for developing systems that are not only technically sound but also socially responsible.
• Long-Term and Non-Behavioral Metrics: Emphasizing metrics that reflect long-term user well-being and diverse socio-cultural impacts highlights the necessity for more refined non-behavioral feedback mechanisms, such as user surveys and qualitative assessments.
• Collaboration and Policy Alignment: There is a substantial need for closer collaboration between academia and industry to address research gaps, especially in causal inference of recommender impacts. Furthermore, better alignment between technical terminology and policy language is crucial to foster effective governance frameworks.

Conclusion

This paper offers a detailed multidisciplinary roadmap for embedding human values into recommender systems. By addressing theoretical constructs, practical methodologies, and policy frameworks, the authors present a robust foundation for developing recommender systems that serve the collective good while respecting individual user values. The synthesis provided here aims to guide future research and development in creating more ethical, accountable, and user-centered recommendation technologies.