A0285
Title: Fairness in machine learning in the presence of missing values
Authors: Aeysha Bhatti - University of Stellenbosch (South Africa) [presenting]
Abstract: The fairness of Machine Learning algorithms is a topic that is receiving increasing attention, as more and more algorithms permeate the day-to-day aspects of our lives. One way in which bias can manifest in a data source is through missing values. If data are missing, these data are often assumed to be missing completely randomly, but usually, this is not the case. In reality, the propensity of data being missing is often tied to socio-economic status or demographic characteristics of individuals. There is very limited research into how missing values and missing value handling methods can impact the fairness of an algorithm. We conduct a systematic study starting from the foundational questions of how the data are missing, how the missing data are dealt with and how this impacts fairness, based on the outcome of a few different types of machine learning algorithms. Most researchers, when dealing with missing data, either apply listwise deletion or tend to use the simpler methods of imputation versus the more complex ones. We study the impact of these simpler methods on the fairness of algorithms. We also investigate the impact of these methods on the trade-off between accuracy and fairness.
