The concept of individual fairness advocates similar treatment of similar individuals to ensure equality in treatment [Dwork et al. 2012]. In this paper, we extend this notion to account for the time at which a decision is made, in settings where there exists a notion of "conduciveness" of decisions as perceived by individuals. We introduce two definitions: (i) fairness-across-time and (ii) fairness-in-hindsight. In the former, treatments of individuals are required to be individually fair relative to the past as well as future, while in the latter we only require individual fairness relative to the past. We show that these two definitions can have drastically different implications in the setting where the principal needs to learn the utility model: one can achieve a vanishing asymptotic loss in long-run average utility relative to the full-information optimum under the fairness-in-hindsight constraint, whereas this asymptotic loss can be bounded away from zero under the fairness-across-time constraint.