  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.
