Fair Classification with Ensembles

Machine learning algorithms are spreading into many essential decision-making procedures of intelligent systems. These algorithms rely on big data which contain several sensitive features regarding the training instances such as gender, age, and race. In some contexts, these features might be useful and relevant like in health or other physiology-related frameworks. However, there are some fields where these features should be ignored like legal, financial, or educational incentivizing decisions. In complex decision-making procedures, it is important to guarantee indifference to these sensitive features. Choosing suitable algorithms or designing decision-making procedures that take this aspect of fairness into account is of crucial importance; especially from the reliability of upcoming intelligent systems which directly affects the impact of the new technology. In this study sample fairness assessment, experiments are performed in three different relevant contexts by applying two different ensemble building methods. It is shown how fairness can be assessed without hurting the accuracy. Furthermore, guidelines are provided for experimental design in order to derive more generic observations regarding incorporating the fairness notion into major classification algorithms
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979-8-3503-0659-0/23/31.002023IEEE