Normalise for Fairness: A Simple Normalisation Technique for Fairness in
Regression Machine Learning Problems
- URL: http://arxiv.org/abs/2202.00993v1
- Date: Wed, 2 Feb 2022 12:26:25 GMT
- Title: Normalise for Fairness: A Simple Normalisation Technique for Fairness in
Regression Machine Learning Problems
- Authors: Mostafa M. Mohamed, Bj\"orn W. Schuller
- Abstract summary: We present a simple, yet effective method based on normalisation (FaiReg) to minimise the impact of unfairness in regression problems.
We compare this method with two standard methods for fairness, namely data balancing and adversarial training.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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