  This thesis scrutinizes common assumptions underlying traditional machine
learning approaches to fairness in consequential decision making. After
challenging the validity of these assumptions in real-world applications, we
propose ways to move forward when they are violated. First, we show that group
fairness criteria purely based on statistical properties of observed data are
fundamentally limited. Revisiting this limitation from a causal viewpoint we
develop a more versatile conceptual framework, causal fairness criteria, and
first algorithms to achieve them. We also provide tools to analyze how
sensitive a believed-to-be causally fair algorithm is to misspecifications of
the causal graph. Second, we overcome the assumption that sensitive data is
readily available in practice. To this end we devise protocols based on secure
multi-party computation to train, validate, and contest fair decision
algorithms without requiring users to disclose their sensitive data or decision
makers to disclose their models. Finally, we also accommodate the fact that
outcome labels are often only observed when a certain decision has been made.
We suggest a paradigm shift away from training predictive models towards
directly learning decisions to relax the traditional assumption that labels can
always be recorded. The main contribution of this thesis is the development of
theoretically substantiated and practically feasible methods to move research
on fair machine learning closer to real-world applications.
