  The issue of fairness in AI arises from discriminatory practices in
applications like job recommendations and risk assessments, emphasising the
need for algorithms that do not discriminate based on group characteristics.
This concern is also pertinent to auctions, commonly used for resource
allocation, which necessitate fairness considerations. Our study examines
auctions with groups distinguished by specific attributes, seeking to (1)
define a fairness notion that ensures equitable treatment for all, (2) identify
mechanisms that adhere to this fairness while preserving incentive
compatibility, and (3) explore the balance between fairness and seller's
revenue. We introduce two fairness notions-group fairness and individual
fairness-and propose two corresponding auction mechanisms: the Group
Probability Mechanism, which meets group fairness and incentive criteria, and
the Group Score Mechanism, which also encompasses individual fairness. Through
experiments, we validate these mechanisms' effectiveness in promoting fairness
and examine their implications for seller revenue.
