  Rankings of people and items are at the heart of selection-making,
match-making, and recommender systems, ranging from employment sites to sharing
economy platforms. As ranking positions influence the amount of attention the
ranked subjects receive, biases in rankings can lead to unfair distribution of
opportunities and resources, such as jobs or income.
  This paper proposes new measures and mechanisms to quantify and mitigate
unfairness from a bias inherent to all rankings, namely, the position bias,
which leads to disproportionately less attention being paid to low-ranked
subjects. Our approach differs from recent fair ranking approaches in two
important ways. First, existing works measure unfairness at the level of
subject groups while our measures capture unfairness at the level of individual
subjects, and as such subsume group unfairness. Second, as no single ranking
can achieve individual attention fairness, we propose a novel mechanism that
achieves amortized fairness, where attention accumulated across a series of
rankings is proportional to accumulated relevance.
  We formulate the challenge of achieving amortized individual fairness subject
to constraints on ranking quality as an online optimization problem and show
that it can be solved as an integer linear program. Our experimental evaluation
reveals that unfair attention distribution in rankings can be substantial, and
demonstrates that our method can improve individual fairness while retaining
high ranking quality.
