  We design fair sponsored search auctions that achieve a near-optimal tradeoff
between fairness and quality. Our work builds upon the model and auction design
of Chawla and Jagadeesan \cite{CJ22}, who considered the special case of a
single slot. We consider sponsored search settings with multiple slots and the
standard model of click through rates that are multiplicatively separable into
an advertiser-specific component and a slot-specific component. When similar
users have similar advertiser-specific click through rates, our auctions
achieve the same near-optimal tradeoff between fairness and quality as in
\cite{CJ22}. When similar users can have different advertiser-specific
preferences, we show that a preference-based fairness guarantee holds. Finally,
we provide a computationally efficient algorithm for computing payments for our
auctions as well as those in previous work, resolving another open direction
from \cite{CJ22}.
