  Algorithmic decision making process now affects many aspects of our lives.
Standard tools for machine learning, such as classification and regression, are
subject to the bias in data, and thus direct application of such off-the-shelf
tools could lead to a specific group being unfairly discriminated. Removing
sensitive attributes of data does not solve this problem because a
\textit{disparate impact} can arise when non-sensitive attributes and sensitive
attributes are correlated. Here, we study a fair machine learning algorithm
that avoids such a disparate impact when making a decision. Inspired by the
two-stage least squares method that is widely used in the field of economics,
we propose a two-stage algorithm that removes bias in the training data. The
proposed algorithm is conceptually simple. Unlike most of existing fair
algorithms that are designed for classification tasks, the proposed method is
able to (i) deal with regression tasks, (ii) combine explanatory attributes to
remove reverse discrimination, and (iii) deal with numerical sensitive
attributes. The performance and fairness of the proposed algorithm are
evaluated in simulations with synthetic and real-world datasets.
