Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models
Sentiment detection is an important building block for multiple information retrieval tasks such as product recommendation, cyberbullying detection, and misinformation detection. Unsurprisingly, multiple commercial APIs, each with different levels of accuracy and fairness, are now available for sentiment detection. While combining inputs from multiple modalities or black-box models for increasing accuracy is commonly studied in multimedia computing literature, there has been little work on combining different modalities for increasing fairness of the resulting decision. In this work, we audit multiple commercial sentiment detection APIs for the gender bias in two actor news headlines settings and report on the level of bias observed. Next, we propose a "Flexible Fair Regression" approach, which ensures satisfactory accuracy and fairness by jointly learning from multiple black-box models. The results pave way for fair yet accurate sentiment detectors for multiple applications.
Code (1)
Tasks
FairnessInformation RetrievalMisinformationProduct RecommendationRetrievalSimilar Papers 제목 키워드 기반
Fairly Accurate: Learning Optimal Accuracy vs. Fairness Tradeoffs for Hate Speech Detection
Recent work has emphasized the importance of balancing competing objectives in model training (e.g., accuracy vs. fairness, or competing measures of fairness). Such trade-offs reflect a broader class of multi-objective o…
FairnessHate Speech DetectionDebiasing Large Language Models in Thai Political Stance Detection via Counterfactual Calibration
Political stance detection in low-resource and culturally complex settings poses a critical challenge for large language models (LLMs). In the Thai political landscape - marked by indirect language, polarized figures, an…
Zero-shot GeneralizationData AugmentationStance DetectionFairness-Enhancing Vehicle Rebalancing in the Ride-hailing System
The rapid growth of the ride-hailing industry has revolutionized urban transportation worldwide. Despite its benefits, equity concerns arise as underserved communities face limited accessibility to affordable ride-hailin…
Demand ForecastingFairnessHuman-Centered Supervision for Sentiment Analysis in Telugu: A Systematic Inquiry Beyond Accuracy
Sentiment analysis for low-resource languages remains challenging in an era where interpretability, human alignment, and fairness are increasingly non-negotiable aspects of modern machine learning systems. These challeng…
Sentiment AnalysisHow Quantization Shapes Bias in Large Language Models
This work presents a comprehensive evaluation of how quantization affects model bias, with particular attention to its impact on individual demographic subgroups. We focus on weight and activation quantization strategies…