Algorithms and decision making based on Big Data have become pervasive in all aspects of our daily (offline and online) lives, as they have become essential tools in personal finance, health care, hiring, housing, education, and policies. Data and algorithms determine the media we consume, the stories we read, the people we meet, the places we visit, but also whether we get a job, or whether our loan request is approved. It is therefore of societal and ethical importance to ask whether these algorithms can be discriminative on grounds, such as gender, ethnicity, marital or health status. It turns out that the answer is positive: for instance, recent studies have shown that Google’s online advertising system displayed ads for high-income jobs to men much more often than it did to women; and ads for arrest records were significantly more likely to show up on searches for distinctively black names or a historically black fraternity.
This algorithmic bias exists even when there is no discrimination intention in the developer of the algorithm. Sometimes it may be inherent to the data sources used (software making decisions based on data can reflect, or even amplify, the results of historical discrimination), but even when the sensitive attributes have been suppressed from the input, a well trained machine learning algorithm may still discriminate on the basis of such sensitive attributes because of correlations existing in the data. One approach is to develop data mining systems which are discrimination-conscious by-design. This is a novel and challenging research area for the data mining community.
The aim of this tutorial is to survey the different aspects of the algorithmic bias problem, presenting its most common variants, with an emphasis on the algorithmic techniques and key ideas developed to derive efficient solutions. The tutorial will cover two main complementary approaches: algorithms for discrimination discovery and discrimination prevention by means of fairness-aware data mining. We will conclude by summarizing the most promising paths for future research. .