Fairness in Recommender Systems: Research Landscape and Future Directions (2205.11127v4)

Abstract: Recommender systems can strongly influence which information we see online, e.g., on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterward, through a review of more than 160 scholarly publications, we present an overview of how research in this field is currently operationalized, e.g., in terms of general research methodology, fairness measures, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science, very abstract problem operationalizations are prevalent and questions of the underlying normative claims and what represents a fair recommendation in the context of a given application are often not discussed in depth. These observations call for more interdisciplinary research to address fairness in recommendation in a more comprehensive and impactful manner.

Fairness in Recommender Systems: Research Landscape and Future Directions

Recommender systems (RS) are critical components within digital platforms, significantly influencing information exposure and impacting user beliefs, decisions, and actions. With the rise of AI technologies in these systems, fairness has emerged as an important aspect, warranting increased attention in recent research endeavors. Despite notable progress, fairness in RS remains an evolving field, highlighted by several research gaps and methodological challenges.

Overview of Notions of Fairness

The paper distinguishes between various notions of fairness, prominently featuring group versus individual fairness, single-sided versus multi-sided fairness, static versus dynamic fairness, and associative versus causal fairness. Group fairness, often associated with statistical parity among protected groups, is contrasted against individual fairness, which advocates for similar treatment of similar individuals. Multi-sided fairness acknowledges the complexity within multi-stakeholder environments, considering consumers and providers alongside other stakeholders. The paper calls for more in-depth investigations into how notions of fairness can be operationalized, particularly through interdisciplinary approaches that incorporate sociotechnical contexts.

Research Contributions and Methodologies

The research contributions predominantly focus on algorithmic development, illustrating a bias towards algorithm-centric technical solutions in fairness-enhancing mechanisms. Most papers present algorithmic adjustments, often in the form of in-process or post-process interventions, aiming to recalibrate recommendation outputs towards pre-defined fairness goals. Interestingly, the common use of MovieLens data in evaluating these algorithms reflects a reliance on widely-available datasets, even if they may not exhibit realistic fairness issues pertinent to diverse problem domains.

Despite an apparent focus on offline evaluations, leveraging computational metrics, the paper notes a significant gap in leveraging dynamic evaluations and causal inference methods. Moreover, there is a scarcity of qualitative approaches, which limits understanding of user-centric fairness perceptions and preferences—a glaring oversight when applied solutions should align with human values and societal ethics.

Implications and Future Research Directions

The implications of this research outline the need for broadening the scope beyond mere technical interventions. More research is necessary to explore dynamic and causal models that account for long-term fairness impacts and interactions between users and RS ecosystems. Real-world applications demand methodologies that transcend static metrics, fostering multi-disciplinary research collaborations to enrich our conceptual frameworks and experimental rigor.

Future directions should prioritize:

• More Human-Centered Evaluations: Conduct user studies and field experiments to ascertain how users perceive fairness within recommendation systems, moving beyond abstract mathematical notions.
• Integration of Societal Constructs: Embed normative assumptions explicitly within fairness models and metrics, ensuring they align with broader ethical standards and user expectations.
• Exploration of Multi-Sided Objective Functions: Develop techniques that balance multiple stakeholders’ needs, mitigating trade-offs between consumer relevance and provider exposure.
• Addressing Intersectionality: Consider compounded biases arising from multiple protected attributes, advancing fairness auditing tools to systematically monitor recommendation outcomes for disparate impacts.

Conclusion

Evaluating fairness in recommender systems is a multi-faceted dilemma, embedded in complex user-platform interactions and societal structures. This paper serves as a catalyst for fostering interdisciplinary collaboration, encouraging deeper explorations into fairness definitions, measurement methodologies, and societal impacts. As AI continues to permeate digital ecosystems, ensuring fairness will necessitate shifts towards broader and more inclusive research paradigms.