Causal Reasoning for Algorithmic Fairness

The success of machine learning algorithms has created a wave of excitement about the problems they could be used to solve. Already we have algorithms that match or outperform humans in nontrivial tasks such as image classification [18], the game of Go [37], and skin cancer classification [15]. This has spurred the use of machine learning algorithms in predictive policing [25], in loan lending [17], and to predict whether released people from jail will re-offend [9]. In these life-changing settings however, it has quickly become clear that machine learning algorithms can unwittingly perpetuate or create discriminatory decisions that are biased against certain individuals (for example, against a particular race, gender, sexual orientation, or other protected attributes). Specifically, such biases have already been demonstrated in natural language processing systems [5] (where algorithms associate men with technical occupations like'computer programmer' and women with domestic occupations like'homemaker'), and in online advertising [41] (where Google showed advertisements suggesting that a person had been arrested when that person had a name more often associated with black individuals).