论文标题
使用多个黑匣子模型在情感检测中平衡公平和准确性
Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models
论文作者
论文摘要
情感检测是多个信息检索任务的重要组成部分，例如产品建议，网络欺凌检测和错误信息检测。毫不奇怪的是，现在有多个具有不同准确性和公平性的商业API可供情感检测。在多媒体计算文献中通常研究了从多种模式或黑盒模型中结合提高精度的输入，但在结合不同模态以提高结果决策的公平性方面几乎没有工作。在这项工作中，我们在两个演员新闻头条环境中为性别偏见审核了多个商业情感检测API，并报告观察到的偏见水平。接下来，我们提出了一种“灵活的公平回归”方法，通过从多个黑色框模型中共同学习，可以确保令人满意的准确性和公平性。结果为多个应用程序铺平了公平而准确的情感探测器。
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.
