A Unified Causal Framework for Auditing Recommender Systems for Ethical Concerns (2409.13210v1)

Abstract: As recommender systems become widely deployed in different domains, they increasingly influence their users' beliefs and preferences. Auditing recommender systems is crucial as it not only ensures the continuous improvement of recommendation algorithms but also safeguards against potential issues like biases and ethical concerns. In this paper, we view recommender system auditing from a causal lens and provide a general recipe for defining auditing metrics. Under this general causal auditing framework, we categorize existing auditing metrics and identify gaps in them -- notably, the lack of metrics for auditing user agency while accounting for the multi-step dynamics of the recommendation process. We leverage our framework and propose two classes of such metrics:future- and past-reacheability and stability, that measure the ability of a user to influence their own and other users' recommendations, respectively. We provide both a gradient-based and a black-box approach for computing these metrics, allowing the auditor to compute them under different levels of access to the recommender system. In our experiments, we demonstrate the efficacy of methods for computing the proposed metrics and inspect the design of recommender systems through these proposed metrics.

• The paper introduces a causal framework that redefines auditing metrics for recommender systems, focusing on user agency, bias, and stability.
• It addresses metric gaps by introducing reachability and stability measures that quantify user control in multi-step recommendation processes.
• Experimental validation on Matrix Factorization and Recurrent Networks demonstrates trade-offs between stability and reachability in ethical auditing.

A Unified Causal Framework for Auditing Recommender Systems for Ethical Concerns

The paper presents a comprehensive causal framework for auditing recommender systems with a focus on addressing ethical concerns such as user agency, bias, and stability. This work is motivated by the increasing influence of recommender systems on users' beliefs and preferences, which necessitates the development of robust auditing methodologies to ensure these systems operate ethically.

Key Contributions

The paper makes several significant contributions:

1. Causal Framework Introduction: The authors propose viewing recommender system auditing through a causal lens, providing a structured approach for defining and categorizing auditing metrics. This perspective facilitates the rigorous analysis of the interactions and dependencies inherent in recommenders' operation.
2. Identification of Metric Gaps: A notable finding is the lack of existing metrics for auditing user agency within the dynamic multi-step recommendation processes. To address this, the authors introduce new classes of metrics that consider both past and future interactions.
3. User-Centric Metrics: The paper defines two principal classes of metrics to quantify user agency:
   • Reachability: Measures a user's ability to influence their future recommendations.
   • Stability: Assesses the sensitivity of a user's recommendations to the actions of adversarial users.
4. Computational Approaches: The research provides both gradient-based and black-box methods to compute these metrics, catering to different levels of system access that an auditor might have.
5. Experimental Validation: The efficacy of the proposed metrics is demonstrated through experiments using well-known recommender systems (Matrix Factorization and Recurrent Recommender Networks) on the MovieLens-1M dataset.

Detailed Discussion

Causal Lens for Auditing

The authors argue that many issues in recommender systems, notably those involving user autonomy, are inherently causal. Traditional metrics often fail to capture the longitudinal dynamics between users and recommender systems, leading to gaps in ethical oversight. By employing a causal framework, the paper provides a foundation for more nuanced and effective auditing tools.

User-Centric Metrics

Reachability

Future-$k$ Reachability is defined as the maximum probability that a user can influence the recommendation of a specific item over the next $k$ steps by modifying their ratings. Past-$k$ Reachability, on the other hand, considers the user's ability to change their recommendation history to affect their current recommendations. These metrics provide auditors with tools to measure how much control a user has over their recommendation outcomes over different time frames.

Stability

Future-$k$ Stability measures the impact that changes in another user's ratings have on a given user's future recommendations. Conversely, Past-$k$ Stability assesses this impact retrospectively. These metrics are critical for understanding the robustness of recommender systems against manipulative behaviors and ensuring that users retain control over their recommendation experiences.

Computational Techniques

The paper explores efficient ways to compute user-centric causal metrics:

• White-Box Access: For scenarios where auditors have access to the system internals, gradient-based optimization methods are proposed. Under certain assumptions about the recomputation of user and item embeddings, the authors show that objectives related to reachability and stability exhibit structures that facilitate efficient optimization.
• Black-Box Access: When auditors lack direct access to system gradients, zeroth-order optimization techniques are employed to approximate the necessary gradients, ensuring that the metrics can still be computed effectively.

Experimental Insights

Empirical evaluations reveal several important trends:

• Stochasticity and Stability: Lower system stochasticity (higher $\beta$ values) leads to more stable recommendations but reduces reachability. This trade-off suggests a need for balance to maintain both diversity and stability in recommendations.
• Algorithm Comparison: Matrix Factorization shows higher reachability but lower stability compared to Recurrent Recommender Networks. This highlights potential differences in ethical considerations between classical and deep learning-based recommendation models.

Implications and Future Directions

The unified causal framework proposed has profound implications for the design and evaluation of recommender systems:

• Ethical Design: The introduction of user-centric causal metrics emphasizes designing recommender systems that respect user agency and are resilient to adversarial manipulations.
• Policy and Regulation: These metrics can inform regulatory bodies about the ethical performance of recommender systems, leading to more robust guidelines and standards.

Future work could explore the practical implementation of these metrics in live systems, further refining the balance between user control and recommendation stability. Additionally, investigating the trade-offs across a broader array of recommender models and real-world scenarios could provide deeper insights into optimizing both user satisfaction and ethical standards.

In conclusion, this paper presents a rigorous and systematic approach to auditing recommender systems, addressing essential ethical concerns, and providing tools and methodologies that could shape the future of responsible AI in recommendation technologies.