Algorithms and Machine Learning (ML) are increasingly affecting everyday life
and several decision-making processes, where ML has an advantage due to
scalability or superior performance. Fairness in such applications is crucial,
where models should not discriminate their results based on race, gender, or
other protected groups. This is especially crucial for models affecting very
sensitive topics, like interview hiring or recidivism prediction. Fairness is
not commonly studied for regression problems compared to binary classification
problems; hence, we present a simple, yet effective method based on
normalisation (FaiReg), which minimises the impact of unfairness in regression
problems, especially due to labelling bias. We present a theoretical analysis
of the method, in addition to an empirical comparison against two standard
methods for fairness, namely data balancing and adversarial training. We also
include a hybrid formulation (FaiRegH), merging the presented method with data
balancing, in an attempt to face labelling and sample biases simultaneously.
The experiments are conducted on the multimodal dataset First Impressions (FI)
with various labels, namely personality prediction and interview screening
score. The results show the superior performance of diminishing the effects of
unfairness better than data balancing, also without deteriorating the
performance of the original problem as much as adversarial training.

通过归一化的方式，该研究提出了一种名为 FaiReg 的简单有效的方法，用于最小化回归问题中的不公平影响，特别是由于标签偏差引起的影响，与数据平衡和对抗训练相比表现更优，同时不会像对抗训练一样破坏原来问题的性能。