View source for Research:Applying Value-Sensitive Algorithm Design to ORES

===Result Explanation and Interpretation===
For all the plots, we will illustrate the meaning of the numbers in the context. For instance, we will show the number of more edits will be labeled as positive per day if we decrease the false positive rate by 10%, e.g., the edits that are supposed to be labeled as positive turn into negative, and how many editors will be affected, e.g., the editors whose edits will be reverted. Potentially using the estimates from prior work, we will further show how many editors will churn due to the revert on their edits, caused by the change of the threshold.

We will also explain the way to interpret the curves on the side of the plot or in some way during the result presentation stage. For instance, in the fairness-error rate plot, we will highlight the key trade-offs, such as the meaning of the curve being steep or flat, which will help stakeholders with the result understanding and decision-making.

We will also explore ways of visualizing trade-offs across more than two types of values, for instance, to show how to achieve the balance among quality control, editor protection, and fairness of subgroups.