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Ranking systems are the key components of modern Information Retrieval (IR)applications, such as search engines and recommender systems. Besides theranking relevance to users, the exposure fairness to item providers has alsobeen considered an important factor in ranking optimization. Many fair rankingalgorithms have been proposed to jointly optimize both ranking relevance andfairness. However, we find that most existing fair ranking methods adopt greedyalgorithms that only optimize rankings for the next immediate session orrequest. As shown in this paper, such a myopic paradigm could limit the upperbound of ranking optimization and lead to suboptimal performance in the longterm.  To this end, we propose \textbf{FARA}, a novel \textbf{F}uture-\textbf{A}ware\textbf{R}anking \textbf{A}lgorithm for ranking relevance and fairnessoptimization. Instead of greedily optimizing rankings for the next immediatesession, FARA plans ahead by jointly optimizing multiple ranklists together andsaving them for future sessions. Specifically, FARA first uses the Taylorexpansion to investigate how future ranklists will influence the overallfairness of the system. Then, based on the analysis of the Taylor expansion,FARA adopts a two-phase optimization algorithm where we first solve an optimalfuture exposure planning problem and then construct the optimal ranklistsaccording to the optimal future exposure planning. Theoretically, we show thatFARA is optimal for ranking relevance and fairness joint optimization.Empirically, our extensive experiments on three semi-synthesized datasets showthat FARA is efficient, effective, and can deliver significantly better rankingperformance compared to state-of-the-art fair ranking methods. We make ourimplementation public at\href{https://github.com/Taosheng-ty/QP_fairness/}{https://github.com/Taosheng-ty/QP\_fairness/}.

FARA: Future-aware Ranking Algorithm for Fairness Optimization