# Debias the Black-box: A Fair Ranking Framework via Knowledge Distillation

arXiv: https://arxiv.org/abs/2208.11628 · HTML: https://mlsanity.com/paper/2208.11628/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General knowledge distillation methods, one of 12 directions in Knowledge distillation.
- Builds on 17 of its 24 references that are in the map.
- First posted on arXiv 24 Aug 2022.

## Placed in

- Knowledge distillation / [General knowledge distillation methods](https://mlsanity.com/distill/general-knowledge-distillation-methods/)

## Builds on (most cited first, 17 of 17)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation](https://mlsanity.com/paper/1404.0736/) (2014)
- [Fairness of Exposure in Rankings](https://mlsanity.com/paper/1802.07281/) (2018)
- [Introducing LETOR 4.0 Datasets](https://mlsanity.com/paper/1306.2597/) (2013)
- [Knowledge Transfer with Jacobian Matching](https://mlsanity.com/paper/1803.00443/) (2018)
- [Policy Learning for Fairness in Ranking](https://mlsanity.com/paper/1902.04056/) (2019)
- [Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System](https://mlsanity.com/paper/1809.07428/) (2018)
- [Evaluating Stochastic Rankings with Expected Exposure](https://mlsanity.com/paper/2004.13157/) (2020)
- [Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness](https://mlsanity.com/paper/2105.00855/) (2021)
- [DE-RRD: A Knowledge Distillation Framework for Recommender System](https://mlsanity.com/paper/2012.04357/) (2020)
- [Bidirectional Distillation for Top-K Recommender System](https://mlsanity.com/paper/2106.02870/) (2021)
- [Unifying Online and Counterfactual Learning to Rank](https://mlsanity.com/paper/2012.04426/) (2020)
- [Collaborative Distillation for Top-N Recommendation](https://mlsanity.com/paper/1911.05276/) (2019)
- [Statistical inference for individual fairness](https://mlsanity.com/paper/2103.16714/) (2021)
- [Dual Correction Strategy for Ranking Distillation in Top-N Recommender System](https://mlsanity.com/paper/2109.03459/) (2021)

## Built on by (most cited first, 1 of 1)

- [Dual-Teacher De-biasing Distillation Framework for Multi-domain Fake News Detection](https://mlsanity.com/paper/2312.01006/) (2023)

## Abstract (arXiv)

Deep neural networks can capture the intricate interaction history information between queries and documents, because of their many complicated nonlinear units, allowing them to provide correct search recommendations. However, service providers frequently face more complex obstacles in real-world circumstances, such as deployment cost constraints and fairness requirements. Knowledge distillation, which transfers the knowledge of a well-trained complex model (teacher) to a simple model (student), has been proposed to alleviate the former concern, but the best current distillation methods focus only on how to make the student model imitate the predictions of the teacher model. To better facilitate the application of deep models, we propose a fair information retrieval framework based on knowledge distillation. This framework can improve the exposure-based fairness of models while considerably decreasing model size. Our extensive experiments on three huge datasets show that our proposed framework can reduce the model size to a minimum of 1% of its original size while maintaining its black-box state. It also improves fairness performance by 15%~46% while keeping a high level of recommendation effectiveness.