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Modeling Bias Evolution in Fashion Recommender Systems: A System Dynamics Approach

Mahsa Goodarzi, M. Abdullah Canbaz

TL;DR

Fashion Recommender Systems can accrue bias through dynamic feedback loops that distort user experience and reinforce stereotypes. The authors deploy a system-dynamics, stock-and-flow framework with experimental simulations to model bias activation and its effects on performance. A key finding is that inductive biases dominate system outcomes over user biases, and while debiasing strategies like data rebalancing and regularization help, they do not fully mitigate bias; expanding the model to include demographics, item diversity, and broader contextual factors is recommended. This work provides a proactive design lens for mitigating bias in FRS and offers a framework that could extend to other dynamic AI systems to promote fairness and inclusivity.

Abstract

Bias in recommender systems not only distorts user experience but also perpetuates and amplifies existing societal stereotypes, particularly in sectors like fashion e-commerce. This study employs a dynamic modeling approach to scrutinize the mechanisms of bias activation and reinforcement within Fashion Recommender Systems (FRS). By leveraging system dynamics modeling and experimental simulations, we dissect the temporal evolution of bias and its multifaceted impacts on system performance. Our analysis reveals that inductive biases exert a more substantial influence on system outcomes than user biases, suggesting critical areas for intervention. We demonstrate that while current debiasing strategies, including data rebalancing and algorithmic regularization, are effective to an extent, they require further enhancement to comprehensively mitigate biases. This research underscores the necessity for advancing these strategies and extending system boundaries to incorporate broader contextual factors such as user demographics and item diversity, aiming to foster inclusivity and fairness in FRS. The findings advocate for a proactive approach in recommender system design to counteract bias propagation and ensure equitable user experiences.

Modeling Bias Evolution in Fashion Recommender Systems: A System Dynamics Approach

TL;DR

Fashion Recommender Systems can accrue bias through dynamic feedback loops that distort user experience and reinforce stereotypes. The authors deploy a system-dynamics, stock-and-flow framework with experimental simulations to model bias activation and its effects on performance. A key finding is that inductive biases dominate system outcomes over user biases, and while debiasing strategies like data rebalancing and regularization help, they do not fully mitigate bias; expanding the model to include demographics, item diversity, and broader contextual factors is recommended. This work provides a proactive design lens for mitigating bias in FRS and offers a framework that could extend to other dynamic AI systems to promote fairness and inclusivity.

Abstract

Bias in recommender systems not only distorts user experience but also perpetuates and amplifies existing societal stereotypes, particularly in sectors like fashion e-commerce. This study employs a dynamic modeling approach to scrutinize the mechanisms of bias activation and reinforcement within Fashion Recommender Systems (FRS). By leveraging system dynamics modeling and experimental simulations, we dissect the temporal evolution of bias and its multifaceted impacts on system performance. Our analysis reveals that inductive biases exert a more substantial influence on system outcomes than user biases, suggesting critical areas for intervention. We demonstrate that while current debiasing strategies, including data rebalancing and algorithmic regularization, are effective to an extent, they require further enhancement to comprehensively mitigate biases. This research underscores the necessity for advancing these strategies and extending system boundaries to incorporate broader contextual factors such as user demographics and item diversity, aiming to foster inclusivity and fairness in FRS. The findings advocate for a proactive approach in recommender system design to counteract bias propagation and ensure equitable user experiences.
Paper Structure (14 sections, 9 figures, 1 table)

This paper contains 14 sections, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Key Concepts: Bias and Recommendation Quality in Fashion Recommender Systems
  3. Methodology
  4. Nonlinear Dynamics of Bias
  5. Model Architecture
  6. Data Management and Design Biases (Green Box):
  7. User Behavior and Interaction Biases (Red Box):
  8. Human-Computer Interaction Dynamics (Pink Box):
  9. Performance and Recommendation Quality (Blue Box):
  10. Experimental Results
  11. Discussion and Final Remarks
  12. Limitations
  13. Closer Look at the Stock and Flow Diagram
  14. Complete list of equations

Figures (9)

  • Figure 1: Feedback Loop for Reinforcing Bias Distribution
  • Figure 2: The Stock and Flow Diagram of Bias in FRS
  • Figure 3: Base-run simulation: bias distribution, FRE recommendations, and HCI metrics over time.
  • Figure 4: Impact of Bias Activation on Performance
  • Figure 5: Impact of different coefficients for the relationship between skewness and bias distribution on quality
  • ...and 4 more figures