Infographic: effect of historical disadvantage on algorithmic selections. A hypothetical AI system is accepting or rejecting loan applications. Its decisions are informed by training data that reflects a historical gender inequality. The inequality was large in the past, but is improving over time. This means that an AI trained on data from 2015 to 2020 will disproportionately reject female applicants, but the problem can be addressed by restricting it to only use recent data. In this infographic, we can adjust the age of the data using a slider. There is a limit to how effective this strategy can be, however, because this also reduces the amount of data available to learn from. If we only accept data from the narrow 2019 to 2020 window, then the model starts making new errors (on both men and women). Rejected due to historical bias. Rejected due to insufficient training data. Suitable man Unsuitable man Suitable woman Unsuitable woman Selected 2015 2017 2019 Training Data Interval: 2015 - 2020