Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics (2407.17459v1)

Abstract: As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (LTR) models. These models rely on the availability of sensitive demographic features such as race or sex. However, in practice, regulatory obstacles and privacy concerns protect this data from collection and use. As a result, practitioners may either need to promote fairness despite the absence of these features or turn to demographic inference tools to attempt to infer them. Given that these tools are fallible, this paper aims to further understand how errors in demographic inference impact the fairness performance of popular fair LTR strategies. In which cases would it be better to keep such demographic attributes hidden from models versus infer them? We examine a spectrum of fair LTR strategies ranging from fair LTR with and without demographic features hidden versus inferred to fairness-unaware LTR followed by fair re-ranking. We conduct a controlled empirical investigation modeling different levels of inference errors by systematically perturbing the inferred sensitive attribute. We also perform three case studies with real-world datasets and popular open-source inference methods. Our findings reveal that as inference noise grows, LTR-based methods that incorporate fairness considerations into the learning process may increase bias. In contrast, fair re-ranking strategies are more robust to inference errors. All source code, data, and experimental artifacts of our experimental study are available here: https://github.com/sewen007/hoiltr.git

Essay on "Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics"

The increasing deployment of learning-to-rank (LTR) systems in decision-making contexts necessitates a careful consideration of fairness, particularly when sensitive demographic data is not accessible due to privacy regulations or legal constraints. The paper entitled "Hidden or Inferred: Fair Learning-To-Rank with Unknown Demographics," examines the dilemma of whether to conceal demographic information or infer it when designing fair LTR strategies. It addresses the potential impact of errors made during demographic inference on fairness and utility metrics within ranking models.

Research Focus and Methods

The primary focus of this research is on the efficacy and fairness of LTR systems when they are obliged to operate with unknown or inferred demographic data. The authors consider a variety of strategies including fairness-aware LTR models and fairness-unaware models followed by fair re-ranking methods. The paper presents a systematic comparison of these models with either hidden or inferred demographic information.

The authors utilize different strategies for examining these models, employing empirical simulations to model inference errors. Additionally, the work includes three case studies using real-world datasets and open-source inference tools to obtain sensitive demographic attributes. This comprehensive experimental setup allows the authors to explore how noise in inferred data impacts the fairness and utility of LTR systems.

Key Findings and Implications

Their findings reveal significant distinctions in the robustness of fair LTR and fair re-ranking strategies to demographic inference errors:

• Robustness of Fair Re-ranking Methods: The research indicates that fair re-ranking methods are more resilient to errors in demographic inference than fairness-aware LTR models. This suggests that under conditions of high inference error, applying fair re-ranking algorithms might ensure more consistent fairness performance.
• Impact of Inference Errors: In models that incorporate demographic attributes, increased inference noise can lead to a paradoxical increase in bias, showcasing a trade-off between fairness and utility which can be disrupted by low-quality inference tools.
• Guidelines for Practitioners: The results emphasize caution in the choice of demographic inference tools, suggesting that practitioners may achieve a higher degree of fairness by adopting re-ranking strategies over LTR approaches when inference errors exceed a certain threshold (estimated around 10%).

These findings have both practical and theoretical implications. Practically, they offer a guideline for the deployment of LTR systems in scenarios where achieving fairness is contingent on the manipulation of sensitive demographic data. The work prompts consideration of the reliability of demographic prediction tools, urging practitioners to carefully weigh the impact of potential errors on both fairness and system utility.

Speculations and Future Directions

This paper provides fertile ground for further exploration of fair LTR systems and resilience to inference errors. Future research directions could include a deeper exploration of multi-valued demographic attributes, as well as broader demographics beyond sex, like race or religion, to understand how various demographic factors interact and impact fairness in machine learning systems. Additionally, innovative approaches that rely on latent representations rather than explicit demographic attributes may offer promising avenues to mitigate privacy concerns while ensuring fairness.

In summary, the paper systematically addresses an important concern in the field of fair machine learning, providing valuable insights and practical guidance on managing unknown demographics in learning-to-rank systems. Through careful empirical and real-world evaluations, the authors illustrate the subtleties of fairness maintenance in the absence or inaccuracy of demographic data, shedding light on solutions that prioritize fairness without compromising utility.