There are cases (e.g., systems with active data collection or significant feedback loops) where the context in which the algorithm operates is critical for understanding its impact. In these cases, the fairness of algorithmic decisions ideally would be analyzed with greater consideration...

The ML-fairness-gym simulates sequential decision making using Open AI’s Gym framework. In this framework, agents interact with simulated environments in a loop. At each step, an agent chooses an action that then affects the environ...

In order to facilitate algorithmic development with this broader context, we have released ML-fairness-gym, a set of components for building simple simulations that explore potential long-run impacts of deploying machine learning-based decis...

Our long-term analysis found two results. First, as found by Liu et al., the equal opportunity agent (EO agent) overlends to the disadvantaged group (group 2, which initially has a lower average credit score) by sometimes applying a lower threshold for the group than would be app...

Since Liu et al.’s original formulation of the lending problem examined only the short-term consequences of the bank’s policies — including short-term profit-maximizing policies (called the max reward agent) and policies subject to an equality of opportunity (EO) constraint — we use the ML-fairne...

Our paper extends the analysis of two other scenarios that have been previously studied in the academic ML fairness literature. The ML-fairness-gym framework is also flexible enough...