Recommendation or Discrimination?: Quantifying Distribution Parity in Information Retrieval Systems
Information retrieval (IR) systems often leverage query data to suggest relevant items to users. This introduces the possibility of unfairness if the query (i.e., input) and the resulting recommendations unintentionally correlate with latent factors that are protected variables (e.g., race, gender, and age). For instance, a visual search system for fashion recommendations may pick up on features of the human models rather than fashion garments when generating recommendations. In this work, we introduce a statistical test for "distribution parity" in the top-K IR results, which assesses whether a given set of recommendations is fair with respect to a specific protected variable. We evaluate our test using both simulated and empirical results. First, using artificially biased recommendations, we demonstrate the trade-off between statistically detectable bias and the size of the search catalog. Second, we apply our test to a visual search system for fashion garments, specifically testing for recommendation bias based on the skin tone of fashion models. Our distribution parity test can help ensure that IR systems' results are fair and produce a good experience for all users.
Code (0)
등록된 구현이 없습니다.
Tasks
Information RetrievalRetrievalSimilar Papers 제목 키워드 기반
Exploring Artist Gender Bias in Music Recommendation
Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i.e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users' …
Collaborative FilteringMusic RecommendationRecommendation SystemsUser Fairness in Recommender Systems
Recent works in recommendation systems have focused on diversity in recommendations as an important aspect of recommendation quality. In this work we argue that the post-processing algorithms aimed at only improving dive…
DiversityFairnessRecommendation SystemsOn conditional parity as a notion of non-discrimination in machine learning
We identify conditional parity as a general notion of non-discrimination in machine learning. In fact, several recently proposed notions of non-discrimination, including a few counterfactual notions, are instances of con…
BIG-bench Machine LearningcounterfactualQuantifying Feature Contributions to Overall Disparity Using Information Theory
When a machine-learning algorithm makes biased decisions, it can be helpful to understand the sources of disparity to explain why the bias exists. Towards this, we examine the problem of quantifying the contribution of e…
AttributeDecision MakingDisparity-preserved Deep Cross-platform Association for Cross-platform Video Recommendation
Cross-platform recommendation aims to improve recommendation accuracy through associating information from different platforms. Existing cross-platform recommendation approaches assume all cross-platform information to b…