We study a multi-period workforce allocation problem in which worker availability is uncertain, tasks may be liked or disliked, and repeated exposure to disliked tasks leads to productivity degradation. To balance robustness, performance, and equity, we introduce an adaptive robust optimization model that incorporates a temporal notion of fairness ensuring that no worker is persistently favored or penalized over time. Worker assignments, productivity levels, and shortage decisions are modeled as adaptive policies responding to realized availability. To obtain a tractable formulation, we approximate these policies with affine decision rules and reformulate each robust constraint through dualization of the underlying uncertainty set, yielding a deterministic linear program. Computational results on representative instances show that the approach is fast to solve, generates allocations that remain feasible under severe availability disruptions, reduces long-term productivity loss, and enforces fairness with limited additional cost.